--------------------------------------------------------------------------------------
      name:  <unnamed>
       log:  /Users/lee/Dropbox/Datavers/Structure.log
  log type:  text
 opened on:  24 Jun 2019, 14:34:11

.         set scheme  plottig 

.         set more off

. 
.         *global dir ="C:\Users\jgw12\Dropbox\Research\Structure\Data\Datavers" /* wo
> rking directory */
.         cd "/Users/lee/Dropbox/Datavers"
/Users/lee/Dropbox/Datavers

.         global dir : pwd

.         cd "$dir"
/Users/lee/Dropbox/Datavers

.         mkdir "$dir/golden"

.         
.            * clean using data *
.         use "V-Dem-DS-CY+Others-v7.1.dta", clear  /* downloaded from https://www.v-d
> em.net/en/data/data-version-7-1/ on 8.24.17 */
(Written by R)

.         keep COWcode year country_name historical_date e_fh_pr e_fh_cl v2x_polyarchy
>  v2x_api v2x_mpi v2x_libdem v2x_liberal v2x_partipdem v2x_partip ///
>                 v2x_delibdem v2xdl_delib v2x_egaldem v2x_egal v2x_veracc v2x_horacc 
> v2exdfdshs v2exdfcbhs  v2exdfvths v2exdfdmhs v2exdfpphs v2dlconslt* ///
>                 v2xps_party v2psorgs v2psprbrch v2psprlnks v2pscohesv v2psnatpar v2e
> xrescon v2lgotovst v2expathhs v2expathhg v2exrmhsol_2 ///
>                 v2exrmhsol_4 v2exrmhgnp_2 v2exrmhgnp_4 v2exctlhs_0 v2exctlhs_2 v2exc
> tlhs_4 v2exctlhg_0  v2exctlhg_2 v2exctlhg_4 v2lginvstp v2lgotovst

.         tab country_name if COWcode==.

                  Country name |      Freq.     Percent        Cum.
-------------------------------+-----------------------------------
             Palestine/British |         31        9.84        9.84
                Palestine/Gaza |         29        9.21       19.05
           Palestine/West Bank |         52       16.51       35.56
                    Somaliland |         86       27.30       62.86
                      Zanzibar |        117       37.14      100.00
-------------------------------+-----------------------------------
                         Total |        315      100.00

.         drop if COWcode==.
(315 observations deleted)

.         tsset COWcode year
       panel variable:  COWcode (unbalanced)
        time variable:  year, 1900 to 2016, but with gaps
                delta:  1 unit

.         local var = "e_fh_pr e_fh_cl v2x_polyarchy v2x_api v2x_mpi v2x_libdem v2x_li
> beral v2x_partipdem v2x_partip v2x_delibdem v2xdl_delib v2x_egaldem v2x_egal v2x_ver
> acc v2x_horacc  v2exdfdshs v2exdfcbhs  v2exdfvths v2exdfdmhs v2exdfpphs v2dlconslt v
> 2dlconslt_mean v2dlconslt_nr v2dlconslt_ord v2dlconslt_osp"

.         foreach v of local var {
  2.                 gen l`v' =l.`v'
  3.                 replace `v' = l`v'  /* change from Dec 31 to Jan 1 dating */
  4.                 drop l`v'
  5.         }
(10,354 missing values generated)
(1,326 real changes made, 175 to missing)
(10,354 missing values generated)
(1,325 real changes made, 175 to missing)
(687 missing values generated)
(16,791 real changes made, 189 to missing)
(687 missing values generated)
(16,791 real changes made, 189 to missing)
(687 missing values generated)
(9,532 real changes made, 189 to missing)
(688 missing values generated)
(16,790 real changes made, 189 to missing)
(293 missing values generated)
(17,182 real changes made, 186 to missing)
(687 missing values generated)
(16,791 real changes made, 189 to missing)
(192 missing values generated)
(17,186 real changes made, 186 to missing)
(688 missing values generated)
(16,790 real changes made, 189 to missing)
(190 missing values generated)
(17,285 real changes made, 186 to missing)
(687 missing values generated)
(16,791 real changes made, 189 to missing)
(290 missing values generated)
(17,185 real changes made, 186 to missing)
(414 missing values generated)
(17,061 real changes made, 186 to missing)
(414 missing values generated)
(17,061 real changes made, 186 to missing)
(190 missing values generated)
(17,285 real changes made, 186 to missing)
(190 missing values generated)
(17,285 real changes made, 186 to missing)
(193 missing values generated)
(17,282 real changes made, 186 to missing)
(196 missing values generated)
(17,279 real changes made, 186 to missing)
(196 missing values generated)
(17,279 real changes made, 186 to missing)
(190 missing values generated)
(17,285 real changes made, 186 to missing)
(190 missing values generated)
(11,455 real changes made, 186 to missing)
(190 missing values generated)
(1,051 real changes made, 186 to missing)
(190 missing values generated)
(1,194 real changes made, 186 to missing)
(190 missing values generated)
(17,285 real changes made, 186 to missing)

.         keep if year>1945
(6,163 observations deleted)

.         gen fh_scale = e_fh_pr + e_fh_cl
(4,191 missing values generated)

.         rename country_name vdem_country

.         rename COWcode cowcode

.         recode cowcode (679=678) (316=315)  /* Yemen (North) and Czechoslovakia get 
> new codes */
(cowcode: 142 changes made)

.         sort cowcode year

.         saveold vdem, replace
(saving in Stata 13 format)
(FYI, saveold has options version(12) and version(11) that write files in older
      Stata formats)
(note: file vdem.dta not found)
file vdem.dta saved

. 
.         import excel p4v2016,clear firstrow  /* download from http://www.systemicpea
> ce.org/inscr/p4v2016.xls on 8.24.17 */

.         rename ccode cowcode

.         tsset cow year
       panel variable:  cowcode (unbalanced)
        time variable:  year, 1800 to 2016, but with gaps
                delta:  1 unit

.         local var = "democ autoc polity2 xrreg xrcomp xropen xconst parreg parcomp e
> xrec exconst polcomp"

.         foreach v of local var {
  2.                 gen l`v' =l.`v'
  3.                 replace `v' = l`v'  /* change from Dec 31 to Jan 1 dating */
  4.                 drop l`v'
  5.         }
(201 missing values generated)
(1,294 real changes made, 201 to missing)
(201 missing values generated)
(1,365 real changes made, 201 to missing)
(433 missing values generated)
(1,649 real changes made, 253 to missing)
(201 missing values generated)
(984 real changes made, 201 to missing)
(201 missing values generated)
(1,014 real changes made, 201 to missing)
(201 missing values generated)
(884 real changes made, 201 to missing)
(201 missing values generated)
(1,252 real changes made, 201 to missing)
(201 missing values generated)
(1,062 real changes made, 201 to missing)
(201 missing values generated)
(1,168 real changes made, 201 to missing)
(402 missing values generated)
(1,102 real changes made, 205 to missing)
(201 missing values generated)
(1,252 real changes made, 201 to missing)
(327 missing values generated)
(1,262 real changes made, 208 to missing)

.         keep if year>1945
(7,598 observations deleted)

.         recode cowcode (347=345) (364=365) (529=530) (679=678) (769=770) (818=816)
(cowcode: 179 changes made)

.         keep cowcode country year democ autoc polity2 xrreg xrcomp xropen xconst par
> reg parcomp exrec exconst polcomp

.         rename country polity_country

.         sort cowcode year

.         saveold polity,replace
(saving in Stata 13 format)
(FYI, saveold has options version(12) and version(11) that write files in older
      Stata formats)
(note: file polity.dta not found)
file polity.dta saved

. 
.         import excel uds_summary,clear firstrow /* downloaded from http://www.unifie
> d-democracy-scores.org/uds.html on 8.24.17 */

.         keep cowcode country year mean

.         tsset cow year
       panel variable:  cowcode (unbalanced)
        time variable:  year, 1946 to 2012, but with gaps
                delta:  1 unit

.         gen lmean = l.mean
(203 missing values generated)

.         replace mean = lmean
(9,850 real changes made, 203 to missing)

.         drop lmean

.         rename mean uds_mean

.         rename country uds_country

.         sort cow year

.         saveold uds, replace
(saving in Stata 13 format)
(FYI, saveold has options version(12) and version(11) that write files in older
      Stata formats)
(note: file uds.dta not found)
file uds.dta saved

. 
.         capture program drop rmerge

.         program define rmerge
  1.                         sort cow year
  2.                         merge cow year using GWFtscs
  3.                         tab _merge
  4.                         rename gwf_military Military
  5.                         rename gwf_pers Personalist
  6.                         rename gwf_party Party
  7.                         rename gwf_mon Monarchy
  8.                         recode Party (1=0) if cow==630  /* Iran */
  9.                         recode Monarch (0=1) if cow==630 & year<1980
 10.                         recode Party (1=0) if gwf_regime=="oligarchy"
 11.                         drop _merge
 12.                         sort cow year
 13.                         merge cow year using weeks
 14.                         tab _merge
 15.                         replace persrat_1 =. if persrat_1<0 | persrat_1>1
 16.                         replace milrat_1 =. if milrat_1<0 | milrat_1>1
 17.                         gen ipers1=round(persrat_1, 0.01)  
 18.                         gen imil1=round(milrat_1, 0.01)
 19.                         corr ipers1 imil1  
 20.                         gen weeks =strongman if strongman~=.
 21.                         replace weeks =2 if junta==1 & junta~=.
 22.                         replace weeks =3 if boss==1 & boss~=.
 23.                         replace weeks =4 if machine==1 & machine~=.
 24.                         replace weeks=5 if monarchy==1 
 25.                         replace weeks=6 if other==1 
 26.                         tab weeks
 27.                         replace weeks=. if weeks<=0 | weeks>6
 28.                         label define wks  1 "Strongman" 2 "Junta"  3 "Boss"  4 "M
> achine"  5 "Monarchy" 6 "Other"
 29.                         label values weeks wks
 30.                         label var weeks "Weeks typology"
 31.                         tab weeks
 32.                         rename other Other
 33.                         rename boss Boss
 34.                         rename strong Strongman
 35.                         rename junta Junta
 36.                         rename machine Machine
 37.                         drop if gwf_fail==.
 38.                         drop _merge
 39.                         
.                         sort cow year
 40.                         merge cow year using SvolikInstitutions
 41.                         tab _merge
 42.                         rename _merge merge
 43.                         gen repeat = year==year[_n-1] if cow==cow[_n-1]
 44.                         list gwf_casename year if repeat==1
 45.                         drop if repeat==1
 46.                         drop repeat
 47.                         ** recode Svolik to Jan 1**
.                         tsset cow year
 48.                         sort cow year
 49.                         gen lag_military = military[_n-1] if cow == cow[_n-1]
 50.                         gen lag_executive = executive[_n-1] if cow == cow[_n-1]
 51.                         gen lag_legislative = legislative[_n-1] if cow == cow[_n-
> 1]
 52.                         gen lag_party = party[_n-1] if cow == cow[_n-1]
 53.                         gen lag_lparty = lparty[_n-1] if cow == cow[_n-1]
 54. 
.                         replace military = lag_military
 55.                         replace executive = lag_executive
 56.                         replace legislative = lag_legislative
 57.                         replace party = lag_party
 58.                         replace lparty = lag_lparty
 59.                         drop lag_*
 60. 
.                         **Svolik institutions data**
.                         gen sv_military = military~="civilian" if military~=""
 61.                         gen sv_mil_corp =  military=="corporate" if military~=""
 62.                         gen sv_mil_pers =  military=="personal" if military~=""
 63.                         gen sv_mil_indir =  military=="indirect" if military~=""
 64. 
.                         gen sv_exec_unelected = executive=="unelected" if executive~
> =""
 65.                         gen sv_exec_select = executive=="selected by a small, une
> lected body" if executive~=""
 66.                         gen sv_exec_oneparty = executive=="" if executive~=""
 67.                         gen sv_exec_75more = executive=="elected by more than 75%
> " if executive~=""
 68.                         gen sv_exec_75less = executive=="elected by less than 75%
> " if executive~=""
 69.                         label var executive "Svolik executive"
 70. 
.                         gen sv_leg_none =legislative=="none" if legislative ~=""
 71.                         gen sv_leg_unelected = legislative=="unelected or appoint
> ed" if legislative ~=""
 72.                         gen sv_leg_oneparty = legislative=="one party or candidat
> e per seat" if legislative ~=""
 73.                         gen sv_leg_75more = legislative=="largest party controls 
> more than 75% of seats" if legislative ~=""
 74.                         gen sv_leg_75less = legislative=="largest party controls 
> less than 75% of seats" if legislative ~=""
 75.                         gen sv_leg_nonpartisan = legislative=="nonpartisan"
 76. 
.                         gen sv_legindex = 0 
 77.                         replace sv_legindex = 1 if sv_leg_unelected==1
 78.                         replace sv_legindex = 2 if sv_leg_oneparty==1
 79.                         replace sv_legindex = 3 if sv_leg_75more==1
 80.                         replace sv_legindex = 4 if sv_leg_75less==1
 81.                         replace sv_legindex = 5 if sv_leg_nonpartisan==1
 82.                         replace sv_legindex = sv_legindex+1
 83.                         label var legislative "Svolik legislatures"
 84.                         replace legislative = "incumbent >75% of seats" if legisl
> ative == "largest party controls more than 75% of seats"
 85.                         replace legislative = "incumbent <75% of seats" if legisl
> ative == "largest party controls less than 75% of seats"
 86. 
.                         gen sv_parties = party=="single" if party~=""
 87.                         replace sv_parties = 2 if party=="multiple"
 88.                         gen sv_party = party=="single" | party=="multiple" if par
> ty~=""
 89.         end

.          
.                 * Variable list for EFA *
.                 global allvar1 ="partyrbrstmp militrank ldrrotation milconsult milme
> rit_mil milmeritpers milnotrial plebiscite heirclan officepers"

.                 global allvar2 ="paramilpers ParamilParty ParamilFReb supportparty p
> artyleader localorgzns partymins excomcivn multiethnic"

.                 global allvar3 ="monoethnic heirparty heirfamily legcompetn leaderre
> latvs leaderciv leadermil leaderrebel heirciv cabciv cabmil partymilit"

.                 global allvar4 ="ldrPriorD ldrParty ldrMil ldrRebel ldrCiv ldrOth ld
> rForgn ldrHered SeizCoup SeizRebel"

.                 global allvar5 ="SeizUpris SeizElec SeizSucc SeizFam"

.                 global allvar6 ="PartyhNoWin PartyhWin PartyhReb PartyhPriorDem Part
> yhNoparty PartyhElec"

.                 global allvar7 ="MilPartyAlly MilPartyNo MilPartyPrior nomilitary mi
> lethnic_incl milethnic_hetero milethnic_homo sectyapp_party"

.                 global allvar8 ="sectyapppers ElecldrPrDict ElecldrPrDem ElecldrNot 
> Elecldr1C Elecldr1F ElecldrMLeg ElecldrMExec"

.                 global allvar9 ="legnoms_indirect legnoms_veto legnoms_noveto legnom
> s_priordem LdrexHighR LdrexLowR LdrexRebel LdrexDemEl LdrexParty"

.                 global allvar10 ="LdrexLoyal LdrexReltv LdrexRulFam LdrexOther party
> excompers partyexcom_faction partyexcom_oppose createparty"

. 
. 
.                 * get data, merge, summarize variables *
.                 use GWF, clear

.                 sort cow year

.                 merge cow year using GWFtscs
(note: you are using old merge syntax; see [D] merge for new syntax)

.                 tab _merge

     _merge |      Freq.     Percent        Cum.
------------+-----------------------------------
          3 |      4,591      100.00      100.00
------------+-----------------------------------
      Total |      4,591      100.00

.                 drop _merge

.                 rmerge
(note: you are using old merge syntax; see [D] merge for new syntax)

     _merge |      Freq.     Percent        Cum.
------------+-----------------------------------
          3 |      4,591      100.00      100.00
------------+-----------------------------------
      Total |      4,591      100.00
(Party: 31 changes made)
(Monarchy: 0 changes made)
(Party: 66 changes made)
(note: you are using old merge syntax; see [D] merge for new syntax)
variables cowcode year do not uniquely identify observations in weeks.dta

     _merge |      Freq.     Percent        Cum.
------------+-----------------------------------
          1 |        695        8.77        8.77
          2 |      3,337       42.09       50.85
          3 |      3,897       49.15      100.00
------------+-----------------------------------
      Total |      7,929      100.00
(0 real changes made)
(1 real change made, 1 to missing)
(5,282 missing values generated)
(3,015 missing values generated)
(obs=2,503)

             |   ipers1    imil1
-------------+------------------
      ipers1 |   1.0000
       imil1 |   0.0329   1.0000

(695 missing values generated)
(410 real changes made)
(689 real changes made)
(718 real changes made)
(520 real changes made)
(1,191 real changes made)

      weeks |      Freq.     Percent        Cum.
------------+-----------------------------------
        -42 |          1        0.01        0.01
          0 |      3,066       42.38       42.40
          1 |        637        8.81       51.20
          2 |        410        5.67       56.87
          3 |        689        9.52       66.39
          4 |        718        9.93       76.32
          5 |        520        7.19       83.51
          6 |      1,191       16.46       99.97
         28 |          1        0.01       99.99
         94 |          1        0.01      100.00
------------+-----------------------------------
      Total |      7,234      100.00
(3,069 real changes made, 3,069 to missing)

      Weeks |
   typology |      Freq.     Percent        Cum.
------------+-----------------------------------
  Strongman |        637       15.29       15.29
      Junta |        410        9.84       25.14
       Boss |        689       16.54       41.68
    Machine |        718       17.24       58.92
   Monarchy |        520       12.48       71.40
      Other |      1,191       28.60      100.00
------------+-----------------------------------
      Total |      4,165      100.00
(3,337 observations deleted)
(note: you are using old merge syntax; see [D] merge for new syntax)
variables cowcode year do not uniquely identify observations in the master data
(note: variable cowcode was int, now float to accommodate using data's values)

     _merge |      Freq.     Percent        Cum.
------------+-----------------------------------
          1 |        461        8.69        8.69
          2 |        715       13.47       22.16
          3 |      4,131       77.84      100.00
------------+-----------------------------------
      Total |      5,307      100.00
(228 missing values generated)

      +--------------------+
      | gwf_casen~e   year |
      |--------------------|
3430. | Yemen 78-NA   1990 |
      +--------------------+
(1 observation deleted)
       panel variable:  cowcode (unbalanced)
        time variable:  year, 1946 to 2010, but with gaps
                delta:  1 unit
(521 missing values generated)
(521 missing values generated)
(521 missing values generated)
(521 missing values generated)
(1,394 missing values generated)
(432 real changes made)
(577 real changes made)
(624 real changes made)
(462 real changes made)
(408 real changes made, 195 to missing)
(521 missing values generated)
(521 missing values generated)
(521 missing values generated)
(521 missing values generated)
(521 missing values generated)
(521 missing values generated)
(521 missing values generated)
(521 missing values generated)
(521 missing values generated)
(521 missing values generated)
(521 missing values generated)
(521 missing values generated)
(521 missing values generated)
(521 missing values generated)
(493 real changes made)
(1,721 real changes made)
(597 real changes made)
(795 real changes made)
(156 real changes made)
(5,306 real changes made)
(597 real changes made)
(795 real changes made)
(521 missing values generated)
(2,194 real changes made)
(521 missing values generated)

.                 drop if gwf_fail==.
(715 observations deleted)

.                 
.                 * create new party *
.                 tsset gwf_caseid year
       panel variable:  gwf_caseid (unbalanced)
        time variable:  year, 1946 to 2010
                delta:  1 unit

.                 gen newparty =support==1 & l.support==0

.                 gen yr = year if newparty==1
(4,519 missing values generated)

.                 egen yrs = max(yr), by(gwf_leaderid)
(3624 missing values generated)

.                 tsset gwf_caseid year
       panel variable:  gwf_caseid (unbalanced)
        time variable:  year, 1946 to 2010
                delta:  1 unit

.                 replace newparty=1 if l.newparty==1 & l.gwf_leaderid==gwf_leaderid &
>  year==year[_n-1]+1
(659 real changes made)

.                 gen createparty =militparty_new==1 | (newparty==1  & partyhistory_po
> st==1) 

.                 gen milmerit_persB = milmerit_pers

.                 recode milmerit_persB (2=1) (1=0)  /* create binary for IRT */
(milmerit_persB: 3511 changes made)

.                 
.                 gen inheritparty = (partyhistory_priorwonsupport==1 | partyhistory_p
> riorno | /*
>                         */ partyhistory_insurgent==1 | partyhistory_priordem==1)  if
>  gwf_case_duration==1 | year==1946
(4,311 missing values generated)

.                 egen inh= max(inheritparty),by(gwf_caseid)   /* ensure no within cas
> e variation */

.                 replace inherit = inh
(4,311 real changes made)

.                 drop inh

.                 
.                 replace region = "ssafrica" if region =="sssafrica" | region=="safri
> ca"
(0 real changes made)

.                 
.                 * rename variable for plots *
.                 rename ldr_group_priordem ldrPriorD

.                 rename ldr_group_domparty ldrParty

.                 rename ldr_group_military ldrMil

.                 rename ldr_group_insurgency ldrRebel

.                 rename ldr_group_civsucc ldrCiv

.                 rename ldr_group_other ldrOther

.                 rename ldr_group_foreign ldrForgn

.                 rename ldr_group_hereditary ldrHered

.                 rename militparty_allyparty MilPartyAlly

.                 rename militparty_noparty  MilPartyNo

.                 rename militparty_priorparty MilPartyPrior

.                 rename militparty_newparty MilPartyNew

.                 rename electldr_notelect ElecldrNot

.                 rename electldr_priordict ElecldrPrDict

.                 rename electldr_priordem ElecldrPrDem

.                 rename electldr_1candidate Elecldr1C

.                 rename electldr_1faction  Elecldr1F

.                 rename electldr_multileg  ElecldrMLeg

.                 rename electldr_multiexec  ElecldrMExec

.                 rename ldr_exp_highrank LdrexHighR

.                 rename ldr_exp_lowrank LdrexLowR

.                 rename ldr_exp_rebel LdrexRebel

.                 rename ldr_exp_demelect LdrexDemEl

.                 rename ldr_exp_supportparty LdrexParty

.                 rename ldr_exp_pers_loyal LdrexLoyal

.                 rename ldr_exp_pers_relative LdrexReltv

.                 rename ldr_exp_rulingfamily LdrexRulFam

.                 rename ldr_exp_other LdrexOther

.                 rename paramil_pers paramilpers

.                 rename paramil_party ParamilParty

.                 rename paramil_fightrebel ParamilFReb

.                 rename seizure_coup SeizCoup

.                 rename seizure_rebel SeizRebel

.                 rename seizure_uprising SeizUpris

.                 rename seizure_election SeizElec

.                 rename seizure_succession SeizSucc

.                 rename seizure_family SeizFam

.                 rename partyhistory_noparty PartyhNoparty

.                 rename partyhistory_postseizure PartyhPost

.                 rename partyhistory_priorelection PartyhElec

.                 rename partyhistory_priornosupport PartyhNoWin

.                 rename partyhistory_priorwonsupport PartyhWin

.                 rename partyhistory_insurgent PartyhReb

.                 rename partyhistory_priordem PartyhPriorDem

.                 rename sectyapp_pers sectyapppers

.                 rename partyexcom_pers partyexcompers

.                 rename milmerit_persB milmeritpers

.                 sutex $allvar1 $allvar2 $allvar3 $allvar4 $allvar5 $allvar6 $allvar7
>  $allvar8 $allvar9 $allvar10, minmax long nobs
%------- Begin LaTeX code -------%

\begin{center}
\begin{longtable}{l c c c c c}
\caption{Summary statistics \label{sumstat}}\\
\hline\hline\multicolumn{1}{c}{\textbf{Variable}}
 &\textbf{Mean}
 & \textbf{Std. Dev.}& \textbf{Min.} &  \textbf{Max.} & \textbf{N} \\ \hline
\endfirsthead
\multicolumn{6}{l}{\emph{... table \thetable{} continued}}
\\ \hline\hline\multicolumn{1}{c}{\textbf{Variable}}
 & \textbf{Mean}
 & \textbf{Std. Dev.}& \textbf{Min.} &  \textbf{Max.} & \textbf{N} \\ \hline
\endhead
\hline
\multicolumn{6}{r}{\emph{Continued on next page...}}\\
\endfoot
\endlastfoot
partyrbrstmp & 0.303 & 0.46 & 0 & 1 & 4591\\
militrank & 1.068 & 1.625 & 0 & 4 & 4591\\
ldrrotation & 0.032 & 0.175 & 0 & 1 & 4591\\
milconsult & 0.134 & 0.341 & 0 & 1 & 4591\\
milmerit\_mil & 0.73 & 0.765 & 0 & 2 & 4591\\
milmeritpers & 0.423 & 0.494 & 0 & 1 & 4591\\
milnotrial & 0.365 & 0.482 & 0 & 1 & 4591\\
plebiscite & 0.222 & 0.415 & 0 & 1 & 4591\\
heirclan & 0.373 & 0.484 & 0 & 1 & 4591\\
officepers & 0.644 & 0.479 & 0 & 1 & 4591\\
paramilpers & 0.354 & 0.478 & 0 & 1 & 4591\\
ParamilParty & 0.184 & 0.388 & 0 & 1 & 4591\\
ParamilFReb & 0.088 & 0.283 & 0 & 1 & 4591\\
supportparty & 0.731 & 0.443 & 0 & 1 & 4591\\
partyleader & 0.588 & 0.492 & 0 & 1 & 4591\\
localorgzns & 1.345 & 0.874 & 0 & 2 & 4591\\
partymins & 1.783 & 1.326 & 0 & 3 & 4591\\
excomcivn & 1.543 & 1.206 & 0 & 3 & 4591\\
multiethnic & 0.504 & 0.5 & 0 & 1 & 4591\\
monoethnic & 0.225 & 0.418 & 0 & 1 & 4591\\
heirparty & 0.429 & 0.495 & 0 & 1 & 4591\\
heirfamily & 0.393 & 0.488 & 0 & 1 & 4591\\
legcompetn & 4.166 & 2.971 & 0 & 8 & 4591\\
leaderrelatvs & 0.504 & 0.5 & 0 & 1 & 4591\\
leaderciv & 0.549 & 0.498 & 0 & 1 & 4591\\
leadermil & 0.346 & 0.476 & 0 & 1 & 4591\\
leaderrebel & 0.105 & 0.307 & 0 & 1 & 4591\\
heirciv & 0.92 & 0.983 & 0 & 2 & 4591\\
cabciv & 1.322 & 0.729 & 0 & 2 & 4591\\
cabmil & 0.598 & 0.688 & 0 & 2 & 4591\\
partymilit & 1.083 & 1.566 & 0 & 4 & 4591\\
ldrPriorD & 0.09 & 0.287 & 0 & 1 & 4591\\
ldrParty & 0.237 & 0.425 & 0 & 1 & 4591\\
ldrMil & 0.287 & 0.453 & 0 & 1 & 4591\\
ldrRebel & 0.103 & 0.304 & 0 & 1 & 4591\\
ldrCiv & 0.017 & 0.128 & 0 & 1 & 4591\\
ldrOther & 0.059 & 0.236 & 0 & 1 & 4591\\
ldrForgn & 0.092 & 0.289 & 0 & 1 & 4591\\
ldrHered & 0.114 & 0.318 & 0 & 1 & 4591\\
SeizCoup & 0.303 & 0.46 & 0 & 1 & 4591\\
SeizRebel & 0.221 & 0.415 & 0 & 1 & 4591\\
SeizUpris & 0.037 & 0.19 & 0 & 1 & 4591\\
SeizElec & 0.132 & 0.339 & 0 & 1 & 4591\\
SeizSucc & 0.043 & 0.202 & 0 & 1 & 4591\\
SeizFam & 0.056 & 0.23 & 0 & 1 & 4591\\
PartyhNoWin & 0.077 & 0.266 & 0 & 1 & 4591\\
PartyhWin & 0.019 & 0.138 & 0 & 1 & 4591\\
PartyhReb & 0.16 & 0.367 & 0 & 1 & 4591\\
PartyhPriorDem & 0.239 & 0.426 & 0 & 1 & 4591\\
PartyhNoparty & 0.269 & 0.443 & 0 & 1 & 4591\\
PartyhElec & 0.016 & 0.125 & 0 & 1 & 4591\\
MilPartyAlly & 0.032 & 0.177 & 0 & 1 & 4591\\
MilPartyNo & 0.112 & 0.315 & 0 & 1 & 4591\\
MilPartyPrior & 0.076 & 0.265 & 0 & 1 & 4591\\
nomilitary & 0.041 & 0.198 & 0 & 1 & 4591\\
milethnic\_inclusive & 0.458 & 0.498 & 0 & 1 & 4591\\
milethnic\_hetero & 0.34 & 0.474 & 0 & 1 & 4591\\
milethnic\_homo & 0.162 & 0.368 & 0 & 1 & 4591\\
sectyapp\_party & 0.155 & 0.362 & 0 & 1 & 4591\\
sectyapppers & 0.596 & 0.491 & 0 & 1 & 4591\\
ElecldrPrDict & 0.009 & 0.094 & 0 & 1 & 4591\\
ElecldrPrDem & 0.021 & 0.144 & 0 & 1 & 4591\\
ElecldrNot & 0.362 & 0.481 & 0 & 1 & 4591\\
Elecldr1C & 0.217 & 0.412 & 0 & 1 & 4591\\
Elecldr1F & 0.038 & 0.192 & 0 & 1 & 4591\\
ElecldrMLeg & 0.055 & 0.229 & 0 & 1 & 4591\\
ElecldrMExec & 0.174 & 0.379 & 0 & 1 & 4591\\
legnoms\_indirect & 0.088 & 0.283 & 0 & 1 & 4591\\
legnoms\_veto & 0.374 & 0.484 & 0 & 1 & 4591\\
legnoms\_noveto & 0.114 & 0.318 & 0 & 1 & 4591\\
legnoms\_priordem & 0.019 & 0.136 & 0 & 1 & 4591\\
LdrexHighR & 0.233 & 0.423 & 0 & 1 & 4591\\
LdrexLowR & 0.093 & 0.291 & 0 & 1 & 4591\\
LdrexRebel & 0.11 & 0.313 & 0 & 1 & 4591\\
LdrexDemEl & 0.134 & 0.341 & 0 & 1 & 4591\\
LdrexParty & 0.232 & 0.422 & 0 & 1 & 4591\\
LdrexLoyal & 0.019 & 0.137 & 0 & 1 & 4591\\
LdrexReltv & 0.027 & 0.162 & 0 & 1 & 4591\\
LdrexRulFam & 0.128 & 0.334 & 0 & 1 & 4591\\
LdrexOther & 0.023 & 0.15 & 0 & 1 & 4591\\
partyexcompers & 0.318 & 0.466 & 0 & 1 & 4591\\
partyexcom\_faction & 0.22 & 0.414 & 0 & 1 & 4591\\
partyexcom\_oppose & 0.127 & 0.333 & 0 & 1 & 4591\\
createparty & 0.162 & 0.368 & 0 & 1 & 4591\\
\hline
\end{longtable}
\end{center}
%------- End LaTeX code -------%

.                 
.  ***********************************
.  *** Exploratory Factor Analysis ***
.  ***********************************
.                 qui factor $allvar1 $allvar2 $allvar3 $allvar4 $allvar5 $allvar6 $al
> lvar7 $allvar8 $allvar9 $allvar10,   

.                 screeplot, mean color(blue) ytitle("Eigen values") xtitle("Factors")
>  legend(lab(1 "Eigen values") lab(2  "mean") pos(2) ring(0)) title("")

.                 graph export "$dir/golden/EigenAll.pdf", as(pdf)  replace   
(file /Users/lee/Dropbox/Datavers/golden/EigenAll.pdf written in PDF format)

.          
.                 qui factor $allvar1 $allvar2 $allvar3 $allvar4 $allvar5 $allvar6 $al
> lvar7 $allvar8 $allvar9 $allvar10,factors(4)

.                 rotate, promax(3)  factors(4)  /* This output is in Column 3 of Tabl
> e C-1 for some of the personalist items */

Factor analysis/correlation                      Number of obs    =      4,591
    Method: principal factors                    Retained factors =          4
    Rotation: oblique promax (Kaiser off)        Number of params =        330

    --------------------------------------------------------------------------
         Factor  |     Variance   Proportion    Rotated factors are correlated
    -------------+------------------------------------------------------------
        Factor1  |      9.47279       0.1398
        Factor2  |      8.79996       0.1298
        Factor3  |      6.28702       0.0928
        Factor4  |      4.87660       0.0719
    --------------------------------------------------------------------------
    LR test: independent vs. saturated: chi2(3486)=       . Prob>chi2 =      .

Rotated factor loadings (pattern matrix) and unique variances

    ---------------------------------------------------------------------
        Variable |  Factor1   Factor2   Factor3   Factor4 |   Uniqueness 
    -------------+----------------------------------------+--------------
    partyrbrstmp |   0.4975    0.1619    0.3666   -0.1047 |      0.6272  
       militrank |   0.0081    0.8844   -0.0287   -0.1433 |      0.2216  
     ldrrotation |  -0.0636    0.3092   -0.1913   -0.1002 |      0.8658  
      milconsult |  -0.1820    0.6065   -0.2562   -0.0840 |      0.5336  
    milmerit_mil |  -0.0809    0.0074   -0.5969   -0.1867 |      0.6268  
    milmeritpers |   0.1337    0.0970    0.5224    0.1978 |      0.6834  
      milnotrial |   0.2552    0.2345    0.4467    0.2353 |      0.6630  
      plebiscite |   0.2752    0.1681    0.1973   -0.2685 |      0.7981  
        heirclan |  -0.2160   -0.2191    0.3723    0.2360 |      0.7340  
      officepers |   0.0705   -0.0703    0.6494   -0.0606 |      0.5734  
     paramilpers |  -0.0486   -0.0476    0.5596    0.0156 |      0.6822  
    ParamilParty |   0.3021   -0.0787   -0.2431   -0.0388 |      0.8158  
     ParamilFReb |  -0.0388    0.1791   -0.1902    0.1371 |      0.9085  
    supportparty |   0.9687   -0.0162    0.0696    0.0867 |      0.0684  
     partyleader |   0.7114   -0.1076    0.0572    0.1280 |      0.4601  
     localorgzns |   0.9175   -0.0480    0.0082    0.0883 |      0.1455  
       partymins |   0.8077   -0.0868   -0.0814    0.1272 |      0.2927  
       excomcivn |   0.7473   -0.1425   -0.1257    0.0850 |      0.3497  
     multiethnic |   0.5919   -0.0656   -0.1915   -0.0634 |      0.5635  
      monoethnic |   0.3184    0.0598    0.2992    0.1624 |      0.8121  
       heirparty |   0.5190   -0.2901   -0.4444    0.0516 |      0.3342  
      heirfamily |  -0.3429   -0.1379    0.6085    0.0886 |      0.4760  
      legcompetn |   0.2925   -0.2250    0.0449   -0.3016 |      0.7308  
    leaderrela~s |  -0.1582   -0.2147    0.5200    0.1069 |      0.6660  
       leaderciv |  -0.1467   -0.8105   -0.1452   -0.3641 |      0.1631  
       leadermil |   0.0344    0.9413    0.0665   -0.1470 |      0.1075  
     leaderrebel |   0.1846   -0.1446    0.1323    0.8183 |      0.3045  
         heirciv |   0.1799   -0.5570   -0.3799   -0.0454 |      0.4300  
          cabciv |   0.0453   -0.5539    0.0517   -0.0163 |      0.6831  
          cabmil |   0.0431    0.6432   -0.0919    0.0212 |      0.5905  
      partymilit |   0.4023   -0.2108   -0.3798    0.3307 |      0.4821  
       ldrPriorD |   0.1488   -0.2553    0.0740   -0.4295 |      0.6877  
        ldrParty |   0.2612   -0.2296   -0.4478   -0.0499 |      0.6168  
          ldrMil |  -0.0446    0.8601    0.0115   -0.1607 |      0.2390  
        ldrRebel |   0.1647   -0.1256    0.1134    0.7602 |      0.4042  
          ldrCiv |   0.0624   -0.0537    0.0113   -0.1141 |      0.9773  
        ldrOther |   0.0321   -0.0051    0.2109   -0.0419 |      0.9530  
        ldrForgn |   0.0835   -0.1125    0.0180    0.0166 |      0.9777  
        ldrHered |  -0.7023   -0.4388    0.2300    0.0182 |      0.3462  
        SeizCoup |  -0.0022    0.7692    0.0850   -0.1747 |      0.3785  
       SeizRebel |   0.0526   -0.2473   -0.1042    0.7066 |      0.4388  
       SeizUpris |  -0.0395    0.0175   -0.0771   -0.0694 |      0.9891  
        SeizElec |   0.1902   -0.2887   -0.0267   -0.4634 |      0.6155  
        SeizSucc |   0.0603    0.0602    0.0827   -0.0595 |      0.9834  
         SeizFam |  -0.4848   -0.3034    0.1664   -0.0183 |      0.6851  
     PartyhNoWin |   0.2049    0.0187    0.0254    0.1054 |      0.9508  
       PartyhWin |   0.0634   -0.0181    0.0396   -0.0526 |      0.9908  
       PartyhReb |   0.2307   -0.1290   -0.2458    0.6152 |      0.4773  
    PartyhPrio~m |   0.3337   -0.2248   -0.0804   -0.3935 |      0.6184  
    PartyhNopa~y |  -0.9687    0.0162   -0.0696   -0.0867 |      0.0684  
      PartyhElec |   0.0407   -0.0339    0.1211   -0.0758 |      0.9761  
    MilPartyAlly |   0.1176    0.2311    0.0395   -0.0614 |      0.9370  
      MilPartyNo |  -0.4470    0.5325   -0.3017   -0.1036 |      0.4035  
    MilPartyPr~r |   0.2334    0.3381    0.0190   -0.0234 |      0.8569  
      nomilitary |  -0.1036   -0.2148    0.0312   -0.1373 |      0.9279  
    milethnic_~e |   0.1472    0.1399   -0.3476   -0.0329 |      0.8420  
    milethnic~ro |  -0.1406   -0.1634    0.1564    0.0257 |      0.9361  
    milethnic~mo |   0.0372    0.1361    0.2523    0.0852 |      0.9087  
    sectyapp_p~y |   0.1897   -0.1486   -0.5176    0.1965 |      0.5926  
    sectyapppers |   0.0302   -0.1203    0.6490    0.0206 |      0.5791  
    ElecldrPrD~t |   0.0106   -0.0525    0.0385   -0.0507 |      0.9926  
    ElecldrPrDem |  -0.0042   -0.1300    0.0200   -0.1957 |      0.9409  
      ElecldrNot |  -0.0432    0.3626   -0.3597    0.2971 |      0.6368  
       Elecldr1C |   0.3434    0.1146    0.1832   -0.1293 |      0.8353  
       Elecldr1F |   0.1117   -0.0493    0.0327    0.0719 |      0.9798  
     ElecldrMLeg |   0.0940   -0.1387   -0.0947   -0.1051 |      0.9416  
    ElecldrMExec |   0.1968   -0.0177    0.0763   -0.1319 |      0.9351  
    legnoms_in~t |  -0.0230    0.0041   -0.0684    0.3330 |      0.8805  
    legnoms_veto |   0.2922   -0.0246    0.1541   -0.1961 |      0.8479  
    legnoms_no~o |  -0.1391   -0.2215   -0.1166   -0.1602 |      0.9015  
    legnoms_pr~m |   0.0065   -0.0810    0.0014   -0.1672 |      0.9631  
      LdrexHighR |  -0.0131    0.7938   -0.0823   -0.1429 |      0.3672  
       LdrexLowR |   0.0646    0.3586    0.2020   -0.0495 |      0.8247  
      LdrexRebel |   0.1657   -0.1592    0.1455    0.8242 |      0.2954  
      LdrexDemEl |   0.1926   -0.3024    0.0323   -0.4335 |      0.6348  
      LdrexParty |   0.2521   -0.2794   -0.4675   -0.0277 |      0.5724  
      LdrexLoyal |   0.0503   -0.0198    0.0139   -0.0713 |      0.9909  
      LdrexReltv |   0.0682   -0.0148    0.1650   -0.0135 |      0.9696  
     LdrexRulFam |  -0.7206   -0.4558    0.2422    0.0119 |      0.3049  
      LdrexOther |  -0.0986   -0.0766    0.0489   -0.1064 |      0.9723  
    partyexcom~s |   0.5268    0.1428    0.4398    0.0190 |      0.5720  
    partyexcom~n |   0.2555   -0.1078   -0.2927    0.1172 |      0.7967  
    partyexcom~e |   0.1603   -0.1298   -0.2915    0.0047 |      0.8507  
     createparty |   0.2694    0.3361    0.4150   -0.0193 |      0.6731  
    ---------------------------------------------------------------------

Factor rotation matrix

    --------------------------------------------------
                 | Factor1  Factor2  Factor3  Factor4 
    -------------+------------------------------------
         Factor1 |  0.8049  -0.6535  -0.3694   0.0157 
         Factor2 |  0.5190   0.7499  -0.0995   0.0750 
         Factor3 |  0.2668  -0.1000   0.9235  -0.0165 
         Factor4 |  0.1079  -0.0262   0.0274  -0.9969 
    --------------------------------------------------

.                 estat common

Correlation matrix of the promax(3) rotated common factors

    ------------------------------------------------------
         Factors |  Factor1   Factor2   Factor3   Factor4 
    -------------+----------------------------------------
         Factor1 |        1                               
         Factor2 |   -.1663         1                     
         Factor3 |  -.09963    .07372         1           
         Factor4 |  -.06048    .07378   -.05578         1 
    ------------------------------------------------------

.                 predict pr1 pr2 pr3 pr4
(regression scoring assumed)

Scoring coefficients (method = regression; based on promax(3) rotated factors)

    ------------------------------------------------------
        Variable |  Factor1   Factor2   Factor3   Factor4 
    -------------+----------------------------------------
    partyrbrstmp |  0.02956   0.01561   0.03430  -0.01612 
       militrank |  0.01200   0.03853  -0.05472  -0.01843 
     ldrrotation | -0.00265   0.02239  -0.02892  -0.00036 
      milconsult | -0.01353   0.03084  -0.04464   0.00083 
    milmerit_mil | -0.02515   0.00314  -0.12120  -0.07672 
    milmeritpers |  0.00386   0.01029   0.10052   0.00193 
      milnotrial |  0.00532   0.00712   0.02810   0.00816 
      plebiscite |  0.00725  -0.01201   0.02540  -0.00762 
        heirclan |  0.00548  -0.01023   0.02199   0.00527 
      officepers |  0.01831  -0.01165   0.06807  -0.01611 
     paramilpers | -0.00743  -0.00630   0.06071  -0.00577 
    ParamilParty |  0.02135  -0.00317  -0.02932  -0.01601 
     ParamilFReb | -0.00726   0.01014  -0.02676   0.00840 
    supportparty |  0.54362   0.05421   0.18171   0.02153 
     partyleader |  0.00525  -0.01329   0.01159  -0.00671 
     localorgzns |  0.05691  -0.00926  -0.02160   0.01347 
       partymins |  0.02148  -0.03561  -0.01506   0.02841 
       excomcivn |  0.03420   0.00467  -0.01920  -0.01264 
     multiethnic |  0.10535  -0.06713  -0.08815  -0.03847 
      monoethnic |  0.06240  -0.04513   0.00837   0.03206 
       heirparty |  0.04624  -0.04913  -0.09196   0.00774 
      heirfamily | -0.00789   0.00440   0.09331   0.00055 
      legcompetn |  0.02696  -0.02955   0.00780  -0.08381 
    leaderrela~s | -0.00010  -0.01405   0.05434  -0.00165 
       leaderciv |  0.00000   0.00000   0.00000   0.00000 
       leadermil |  0.05599   0.37763   0.07914   0.10479 
     leaderrebel | -0.00982   0.06331   0.02674   0.24444 
         heirciv |  0.03234  -0.01567  -0.06023  -0.00835 
          cabciv |  0.01177  -0.05241  -0.00187  -0.02903 
          cabmil |  0.00032   0.08198  -0.03022  -0.00398 
      partymilit |  0.01843  -0.00684  -0.03789   0.04485 
       ldrPriorD |  0.06223  -0.13717   0.02173  -0.11438 
        ldrParty |  0.06297  -0.15789  -0.07037   0.00031 
          ldrMil |  0.00000   0.00000   0.00000   0.00000 
        ldrRebel |  0.05174  -0.10420   0.03099   0.20350 
          ldrCiv |  0.01207  -0.04958   0.00430  -0.02995 
        ldrOther |  0.01501  -0.06756   0.04243  -0.00273 
        ldrForgn |  0.04400  -0.11218  -0.01158   0.01265 
        ldrHered | -0.07750  -0.15436   0.02485   0.06248 
        SeizCoup | -0.00517   0.02786  -0.01843  -0.02331 
       SeizRebel |  0.00922  -0.00541  -0.05250   0.09996 
       SeizUpris |  0.00532  -0.01088  -0.01091  -0.00024 
        SeizElec |  0.01785  -0.03379   0.00704  -0.04487 
        SeizSucc |  0.00435  -0.01376   0.00391  -0.00269 
         SeizFam | -0.00854  -0.01218  -0.00391  -0.00854 
     PartyhNoWin | -0.02406  -0.00606  -0.00429   0.00689 
       PartyhWin | -0.03229   0.00137   0.00090  -0.02123 
       PartyhReb | -0.04984  -0.02787  -0.08198   0.09371 
    PartyhPrio~m | -0.03141  -0.03313  -0.03861  -0.12185 
    PartyhNopa~y |  0.00000   0.00000   0.00000   0.00000 
      PartyhElec | -0.03241  -0.00233   0.01122  -0.00592 
    MilPartyAlly | -0.00912  -0.03296   0.00682   0.00764 
      MilPartyNo | -0.02961  -0.02328  -0.03432  -0.01492 
    MilPartyPr~r | -0.01905  -0.02788  -0.00131   0.01136 
      nomilitary | -0.02914  -0.04283   0.02904  -0.04427 
    milethnic_~e |  0.00000   0.00000   0.00000   0.00000 
    milethnic~ro | -0.04107  -0.04660   0.10174   0.03017 
    milethnic~mo | -0.01714   0.00029   0.09859   0.03093 
    sectyapp_p~y | -0.00305  -0.01386  -0.05824   0.04688 
    sectyapppers |  0.00217  -0.01724   0.12102  -0.00074 
    ElecldrPrD~t | -0.01588   0.02700   0.00961   0.01276 
    ElecldrPrDem | -0.00804   0.03249   0.01132  -0.02727 
      ElecldrNot | -0.05656   0.22678  -0.08172   0.17483 
       Elecldr1C |  0.02190   0.17385   0.04729   0.00792 
       Elecldr1F |  0.00065   0.06494   0.01025   0.04608 
     ElecldrMLeg | -0.01808   0.05687  -0.01806   0.01267 
    ElecldrMExec | -0.00798   0.13017   0.02832   0.01117 
    legnoms_in~t | -0.01278  -0.00003  -0.00738   0.01657 
    legnoms_veto |  0.03108   0.00639   0.04111  -0.03948 
    legnoms_no~o | -0.02615  -0.02149  -0.01559  -0.00891 
    legnoms_pr~m | -0.00876  -0.00758   0.00289  -0.00494 
      LdrexHighR |  0.00000   0.00000   0.00000   0.00000 
       LdrexLowR |  0.02258  -0.06089   0.03354  -0.00091 
      LdrexRebel | -0.00583  -0.13948   0.03637   0.24033 
      LdrexDemEl |  0.02671  -0.17858   0.01251  -0.04639 
      LdrexParty |  0.03039  -0.21489  -0.08728   0.08847 
      LdrexLoyal | -0.00713  -0.05501   0.00735   0.00078 
      LdrexReltv | -0.00602  -0.07060   0.04621   0.03527 
     LdrexRulFam | -0.12368  -0.12097   0.11444   0.11300 
      LdrexOther | -0.01064  -0.07134   0.00972   0.00421 
    partyexcom~s |  0.04998   0.01055   0.13758  -0.01259 
    partyexcom~n | -0.01156   0.00017  -0.02764   0.02081 
    partyexcom~e | -0.01989  -0.01491  -0.03213  -0.00040 
     createparty | -0.07490  -0.04340   0.05416  -0.00862 
    ------------------------------------------------------


.                 screeplot, mean color(blue) ytitle("Eigen values") xtitle("Factors")
>  legend(lab(1 "Eigen values") ///
>                         lab(2  "mean") pos(2) ring(0)) title("") neigen(12) yla(,glc
> ol(gs15)) xlab(1(1)12) xscale(range(0.8 12.2))

.                 loadingplot, maxlength(14) note("") mlabel() xtitle(Party) ytitle(Mi
> litary,height(1)) ///
>                         title("       Components of first two dimensions") ylab(,glc
> ol(gs16))

.                 graph export "$dir/golden/Load12.pdf", as(pdf)  replace   
(file /Users/lee/Dropbox/Datavers/golden/Load12.pdf written in PDF format)

.                 loadingplot, factors(3) maxlength(14) combined note("") msymbol(oh) 
> mcolor(red) mlabgap(.25) ///
>                         mlabcolor(blue) mlabsize(2.5) mlabpos(12) title("") ysize(8)
>  xsize(10) ylab(,glcol(gs16))

.                 graph export "$dir/golden/Load123.pdf", as(pdf)  replace   
(file /Users/lee/Dropbox/Datavers/golden/Load123.pdf written in PDF format)

.                 eofplot, factors(1/3) ysc(range(-0.25 0.35)) color(red blue cyan ) x
> lab(1/87,angle(90)) ylab(,glcol(gs15)) ///
>                         legend(lab(1 "Dimension 1 (Party)") lab(2 "Dimension 2 (Mili
> tary)") lab(3 "Dimension 3 (Personal)")pos(12) ring(1) col(3)) ///
>                         xsize(9) ysize(3) ytitle(Loadings, height(1)) note("") yline
> (-.1, lpattern(dash)) yline(.1, lpattern(dash))

.                 graph export "$dir/golden/LoadVars.pdf", as(pdf)  replace   
(file /Users/lee/Dropbox/Datavers/golden/LoadVars.pdf written in PDF format)

.                  
.                 /*qui factor $allvar1 $allvar2 $allvar3 $allvar4 $allvar5 $allvar6 $
> allvar7 $allvar8 $allvar9 $allvar10,factors(6)
>                                 rotate, promax(6)  factors(6)
>                                 estat common
>                 eofplot, factors(1/3) ysc(range(-0.25 0.35)) color(red blue cyan) xl
> ab(1/84,valuelabels angle(90)) ylab(,glcol(gs15)) ///
>                                 legend(lab(1 "Dimension 1 (Party)") lab(2 "Dimension
>  2 (Military)") lab(3 "Dimension 3 (Personal)")pos(12) ring(1) col(3)) ///
>                                 xsize(9) ysize(3) ytitle(Loadings, height(1)) note("
> ") yline(-.3, lpattern(dash)) yline(.3, lpattern(dash))
>                 */                      
.                 matrix m = e(r_L) 

.                 global rows = rowsof(m)

.                 gen n=_n

.                 gen load1 = .
(4,591 missing values generated)

.                 gen load2 = .
(4,591 missing values generated)

.                 gen load3 = .
(4,591 missing values generated)

.                 gen load4 = .
(4,591 missing values generated)

.                 gen varname = ""
(4,591 missing values generated)

.                 forval num=1/4 {
  2.                         forval i=1/$rows {
  3.                                 local rownms: rown m
  4.                                 qui replace load`num'=m[`i',`num'] if n==`i'
  5.                                 local rowname: word `i' of `rownms' 
  6.                                 qui replace varname = "`rowname'" if _n==`i'
  7.                         }
  8.                         sort load`num'
  9.                         gen n_`num'=_n
 10.                         gen varname`num'=varname if _n<=$rows
 11.                         labmask n_`num', values(varname1)
 12.                 }
(4,507 missing values generated)
(4,507 missing values generated)
(4,507 missing values generated)
(4,507 missing values generated)

.                 global cut =.355

.                 twoway scatter load1 n_1 if n_1<=$rows, xtit("")  xlab(1/$rows,value
> labels angle(90)) xsize(9) ysize(3) ytit(Factor 1 loadings, height(1)) note("") ///
>                                 yline(-$cut, lpat(dash)lw(vthin)) yline($cut, lpat(d
> ash) lw(vthin)) ylab(-1(.5)1) saving(h1.gph,replace) tit({bf:Party},ring(0)size(vlar
> ge))
(note: file h1.gph not found)
(file h1.gph saved)

.                 twoway scatter load2 n_2 if n_2<=$rows, xtit("")  xlab(1/$rows,value
> labels angle(90)) xsize(9) ysize(3) ytit(Factor 2 loadings, height(1)) note("") ///
>                                 yline(-$cut, lpat(dash)lw(vthin)) yline($cut, lpat(d
> ash)lw(vthin)) ylab(-1(.5)1) saving(h2.gph,replace) tit({bf:Military},ring(0)size(vl
> arge))
(note: file h2.gph not found)
(file h2.gph saved)

.                 twoway scatter load3 n_3 if n_3<=$rows, xtit("")  xlab(1/$rows,value
> labels angle(90)) xsize(9) ysize(3) ytit(Factor 3 loadings, height(1)) note("") ///
>                                 yline(-$cut, lpat(dash)lw(vthin)) yline($cut, lpat(d
> ash)lw(vthin)) ylab(-1(.5)1) saving(h3.gph,replace) tit({bf:Personal},ring(0)size(vl
> arge))
(note: file h3.gph not found)
(file h3.gph saved)

.                 twoway scatter load4 n_4 if n_4<=$rows, xtit("")  xlab(1/$rows,value
> labels angle(90)) xsize(9) ysize(3) ytit(Factor 4 loadings, height(1)) note("") ///
>                                 yline(-$cut, lpat(dash)lw(vthin)) yline($cut, lpat(d
> ash)lw(vthin)) ylab(-1(.5)1) saving(h4.gph,replace) tit(Rebel)
(note: file h4.gph not found)
(file h4.gph saved)

.                 gr combine h1.gph h2.gph h3.gph, col(1) ysize(9) xsize(10) iscale(.4
> 5)

.                 graph export "$dir/golden/Figure1-LoadVars.pdf", as(pdf)  replace   
(file /Users/lee/Dropbox/Datavers/golden/Figure1-LoadVars.pdf written in PDF format)

. 
.  * IRT Personal *
.                 global pers11="sectyapppers officepers paramilpers milmeritpers miln
> otrial partyexcompers createparty partyrbr leaderrel heirfam heirclan"

.                 global pers10="sectyapppers officepers paramilpers milmeritpers miln
> otrial partyexcompers createparty partyrbr leaderrel heirfam"

.                 global pers8="sectyapppers officepers paramilpers milmeritpers milno
> trial partyexcompers createparty partyrbr"

. 
.                         * 11 variables with (oblique) rotated loading >.3 *
.                 factor $pers11,ipf factors(2)
(obs=4,591)

Factor analysis/correlation                      Number of obs    =      4,591
    Method: iterated principal factors           Retained factors =          2
    Rotation: (unrotated)                        Number of params =         21

    --------------------------------------------------------------------------
         Factor  |   Eigenvalue   Difference        Proportion   Cumulative
    -------------+------------------------------------------------------------
        Factor1  |      2.88866      1.54499            0.6825       0.6825
        Factor2  |      1.34368      0.94055            0.3175       1.0000
        Factor3  |      0.40313      0.16791            0.0953       1.0953
        Factor4  |      0.23522      0.16763            0.0556       1.1508
        Factor5  |      0.06759      0.04351            0.0160       1.1668
        Factor6  |      0.02408      0.08149            0.0057       1.1725
        Factor7  |     -0.05741      0.02468           -0.0136       1.1589
        Factor8  |     -0.08209      0.03283           -0.0194       1.1395
        Factor9  |     -0.11493      0.05709           -0.0272       1.1124
       Factor10  |     -0.17202      0.13157           -0.0406       1.0717
       Factor11  |     -0.30359            .           -0.0717       1.0000
    --------------------------------------------------------------------------
    LR test: independent vs. saturated:  chi2(55) = 1.4e+04 Prob>chi2 = 0.0000

Factor loadings (pattern matrix) and unique variances

    -------------------------------------------------
        Variable |  Factor1   Factor2 |   Uniqueness 
    -------------+--------------------+--------------
    sectyapppers |   0.6544    0.1425 |      0.5514  
      officepers |   0.6684   -0.0281 |      0.5525  
     paramilpers |   0.5400    0.2181 |      0.6608  
    milmeritpers |   0.5078   -0.0148 |      0.7419  
      milnotrial |   0.4904   -0.1580 |      0.7345  
    partyexcom~s |   0.5001   -0.5948 |      0.3961  
     createparty |   0.3578   -0.1235 |      0.8568  
    partyrbrstmp |   0.4792   -0.6298 |      0.3737  
    leaderrela~s |   0.5175    0.4197 |      0.5561  
      heirfamily |   0.5180    0.4147 |      0.5597  
        heirclan |   0.2825    0.3688 |      0.7842  
    -------------------------------------------------

.                 factor $pers11,ml factors(2) 
(obs=4,591)
Iteration 0:   log likelihood = -1164.2269
Iteration 1:   log likelihood =  -950.3541
Iteration 2:   log likelihood = -942.44934
Iteration 3:   log likelihood = -942.29986
Iteration 4:   log likelihood = -942.29676
Iteration 5:   log likelihood = -942.29666
Iteration 6:   log likelihood = -942.29666

Factor analysis/correlation                      Number of obs    =      4,591
    Method: maximum likelihood                   Retained factors =          2
    Rotation: (unrotated)                        Number of params =         21
                                                 Schwarz's BIC    =    2061.66
    Log likelihood = -942.2967                   (Akaike's) AIC   =    1926.59

    --------------------------------------------------------------------------
         Factor  |   Eigenvalue   Difference        Proportion   Cumulative
    -------------+------------------------------------------------------------
        Factor1  |      2.69315      1.14530            0.6350       0.6350
        Factor2  |      1.54786            .            0.3650       1.0000
    --------------------------------------------------------------------------
    LR test: independent vs. saturated:  chi2(55) = 1.4e+04 Prob>chi2 = 0.0000
    LR test:   2 factors vs. saturated:  chi2(34) = 1882.20 Prob>chi2 = 0.0000

Factor loadings (pattern matrix) and unique variances

    -------------------------------------------------
        Variable |  Factor1   Factor2 |   Uniqueness 
    -------------+--------------------+--------------
    sectyapppers |   0.5598    0.3713 |      0.5487  
      officepers |   0.6420    0.2145 |      0.5418  
     paramilpers |   0.4167    0.4201 |      0.6499  
    milmeritpers |   0.4424    0.1914 |      0.7677  
      milnotrial |   0.4663    0.0605 |      0.7789  
    partyexcom~s |   0.7107   -0.4133 |      0.3241  
     createparty |   0.3551    0.0378 |      0.8724  
    partyrbrstmp |   0.6939   -0.4341 |      0.3300  
    leaderrela~s |   0.3571    0.5375 |      0.5836  
      heirfamily |   0.3406    0.5674 |      0.5621  
        heirclan |   0.1568    0.4191 |      0.7998  
    -------------------------------------------------

.                 factor $pers11,pf factors(2)
(obs=4,591)

Factor analysis/correlation                      Number of obs    =      4,591
    Method: principal factors                    Retained factors =          2
    Rotation: (unrotated)                        Number of params =         21

    --------------------------------------------------------------------------
         Factor  |   Eigenvalue   Difference        Proportion   Cumulative
    -------------+------------------------------------------------------------
        Factor1  |      2.85089      1.58152            0.7317       0.7317
        Factor2  |      1.26937      0.85384            0.3258       1.0576
        Factor3  |      0.41553      0.18104            0.1067       1.1642
        Factor4  |      0.23449      0.21398            0.0602       1.2244
        Factor5  |      0.02051      0.04325            0.0053       1.2297
        Factor6  |     -0.02274      0.07031           -0.0058       1.2238
        Factor7  |     -0.09304      0.03250           -0.0239       1.1999
        Factor8  |     -0.12554      0.04748           -0.0322       1.1677
        Factor9  |     -0.17303      0.02640           -0.0444       1.1233
       Factor10  |     -0.19942      0.08155           -0.0512       1.0721
       Factor11  |     -0.28098            .           -0.0721       1.0000
    --------------------------------------------------------------------------
    LR test: independent vs. saturated:  chi2(55) = 1.4e+04 Prob>chi2 = 0.0000

Factor loadings (pattern matrix) and unique variances

    -------------------------------------------------
        Variable |  Factor1   Factor2 |   Uniqueness 
    -------------+--------------------+--------------
    sectyapppers |   0.6438    0.1390 |      0.5661  
      officepers |   0.6598   -0.0275 |      0.5639  
     paramilpers |   0.5376    0.2143 |      0.6651  
    milmeritpers |   0.5250   -0.0226 |      0.7238  
      milnotrial |   0.5112   -0.1803 |      0.7062  
    partyexcom~s |   0.4879   -0.5717 |      0.4351  
     createparty |   0.3635   -0.1340 |      0.8499  
    partyrbrstmp |   0.4611   -0.5922 |      0.4367  
    leaderrela~s |   0.5053    0.4097 |      0.5768  
      heirfamily |   0.5055    0.3984 |      0.5858  
        heirclan |   0.2852    0.3851 |      0.7704  
    -------------------------------------------------

.                 loadingplot, factors(2) combined note("") msymbol(oh) mcolor(red) ml
> abgap(.25) mlabcolor(blue) mlabsize(2.5) ///
>                                 mlabpos(12) title("") yscale(range(-.4 .6)) xlab(0(.
> 2).8) ylab(-.4(.2).6,glcol(gs15)) xtitl("Factor 1",size(medium))  ///
>                                 ytitl("Factor 2",size(medium) height(2))        

.                 graph export "$dir/golden/PersPCALoad.pdf", as(pdf)  replace        
>             
(file /Users/lee/Dropbox/Datavers/golden/PersPCALoad.pdf written in PDF format)

.                 sutex $pers11
%------- Begin LaTeX code -------%

\begin{table}[htbp]\centering \caption{Summary statistics \label{sumstat}}
\begin{tabular}{l c c  }\hline\hline
\multicolumn{1}{c}{\textbf{Variable}} & \textbf{Mean}
 & \textbf{Std. Dev.} \\ \hline
sectyapppers & 0.596 & 0.491  \\
officepers & 0.644 & 0.479  \\
paramilpers & 0.354 & 0.478  \\
milmeritpers & 0.423 & 0.494  \\
milnotrial & 0.365 & 0.482  \\
partyexcompers & 0.318 & 0.466  \\
createparty & 0.162 & 0.368  \\
partyrbrstmp & 0.303 & 0.46  \\
leaderrelatvs & 0.504 & 0.5  \\
heirfamily & 0.393 & 0.488  \\
heirclan & 0.373 & 0.484  \\
\multicolumn{1}{c}{N} & \multicolumn{2}{c}{4591}\\ \hline
\end{tabular}
\end{table}
%------- End LaTeX code -------%

.                 alpha $pers11,gen(alphapers11) std item

Test scale = mean(standardized items)

                                                            average
                             item-test     item-rest       interitem
Item         |  Obs  Sign   correlation   correlation     correlation     alpha
-------------+-----------------------------------------------------------------
sectyapppers | 4591    +       0.6795        0.5749          0.2212      0.7396
officepers   | 4591    +       0.6912        0.5894          0.2196      0.7378
paramilpers  | 4591    +       0.5915        0.4685          0.2331      0.7525
milmeritpers | 4591    +       0.5853        0.4611          0.2340      0.7534
milnotrial   | 4591    +       0.5678        0.4405          0.2364      0.7558
partyexcom~s | 4591    +       0.5189        0.3835          0.2430      0.7625
createparty  | 4591    +       0.4604        0.3168          0.2509      0.7701
partyrbrstmp | 4591    +       0.4916        0.3521          0.2467      0.7661
leaderrela~s | 4591    +       0.5659        0.4382          0.2366      0.7561
heirfamily   | 4591    +       0.5643        0.4363          0.2368      0.7563
heirclan     | 4591    +       0.3827        0.2304          0.2614      0.7797
-------------+-----------------------------------------------------------------
Test scale   |                                               0.2382      0.7747
-------------------------------------------------------------------------------

.                 irt 2pl $pers11

Fitting fixed-effects model:

Iteration 0:   log likelihood = -32269.414  
Iteration 1:   log likelihood =  -32222.12  
Iteration 2:   log likelihood = -32222.103  
Iteration 3:   log likelihood = -32222.103  

Fitting full model:

Iteration 0:   log likelihood = -29301.359  
Iteration 1:   log likelihood = -28401.636  
Iteration 2:   log likelihood =  -28345.24  
Iteration 3:   log likelihood = -28344.176  
Iteration 4:   log likelihood = -28344.176  

Two-parameter logistic model                    Number of obs     =      4,591
Log likelihood = -28344.176
------------------------------------------------------------------------------
             |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
sectyapppers |
     Discrim |   2.499531   .1114019    22.44   0.000     2.281187    2.717875
        Diff |  -.2751554   .0232981   -11.81   0.000    -.3208189    -.229492
-------------+----------------------------------------------------------------
officepers   |
     Discrim |   2.747095   .1303829    21.07   0.000     2.491549    3.002641
        Diff |  -.4156052   .0235719   -17.63   0.000    -.4618053   -.3694052
-------------+----------------------------------------------------------------
paramilpers  |
     Discrim |    1.75965   .0793245    22.18   0.000     1.604177    1.915123
        Diff |   .5312431   .0284956    18.64   0.000     .4753928    .5870935
-------------+----------------------------------------------------------------
milmeritpers |
     Discrim |   1.422732   .0615818    23.10   0.000     1.302034     1.54343
        Diff |   .3095569    .029577    10.47   0.000     .2515871    .3675267
-------------+----------------------------------------------------------------
milnotrial   |
     Discrim |   1.371869   .0618224    22.19   0.000     1.250699    1.493039
        Diff |   .5512832   .0330536    16.68   0.000     .4864993    .6160672
-------------+----------------------------------------------------------------
partyexcom~s |
     Discrim |   1.138525   .0594107    19.16   0.000     1.022082    1.254968
        Diff |   .8450091   .0449525    18.80   0.000     .7569039    .9331143
-------------+----------------------------------------------------------------
createparty  |
     Discrim |    1.23781   .0679424    18.22   0.000     1.104645    1.370975
        Diff |   1.680027   .0716837    23.44   0.000     1.539529    1.820524
-------------+----------------------------------------------------------------
partyrbrstmp |
     Discrim |   1.075448    .057464    18.72   0.000     .9628207    1.188076
        Diff |   .9548353   .0501378    19.04   0.000     .8565671    1.053103
-------------+----------------------------------------------------------------
leaderrela~s |
     Discrim |   1.364393   .0606373    22.50   0.000     1.245546     1.48324
        Diff |  -.0043786   .0292433    -0.15   0.881    -.0616944    .0529372
-------------+----------------------------------------------------------------
heirfamily   |
     Discrim |   1.434369   .0653879    21.94   0.000     1.306211    1.562527
        Diff |    .428603   .0306311    13.99   0.000     .3685671    .4886389
-------------+----------------------------------------------------------------
heirclan     |
     Discrim |   .6443516   .0420526    15.32   0.000       .56193    .7267731
        Diff |   .8836737   .0706709    12.50   0.000     .7451612    1.022186
------------------------------------------------------------------------------

.                 predict irtpers11, latent
(option ebmeans assumed)
(using 7 quadrature points)

.                 estat report $pers11, byparm sort(b)

Two-parameter logistic model                    Number of obs     =      4,591
Log likelihood = -28344.176
--------------------------------------------------------------------------------
               |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
---------------+----------------------------------------------------------------
Discrim        |
    officepers |   2.747095   .1303829    21.07   0.000     2.491549    3.002641
  sectyapppers |   2.499531   .1114019    22.44   0.000     2.281187    2.717875
 leaderrelatvs |   1.364393   .0606373    22.50   0.000     1.245546     1.48324
  milmeritpers |   1.422732   .0615818    23.10   0.000     1.302034     1.54343
    heirfamily |   1.434369   .0653879    21.94   0.000     1.306211    1.562527
   paramilpers |    1.75965   .0793245    22.18   0.000     1.604177    1.915123
    milnotrial |   1.371869   .0618224    22.19   0.000     1.250699    1.493039
partyexcompers |   1.138525   .0594107    19.16   0.000     1.022082    1.254968
      heirclan |   .6443516   .0420526    15.32   0.000       .56193    .7267731
  partyrbrstmp |   1.075448    .057464    18.72   0.000     .9628207    1.188076
   createparty |    1.23781   .0679424    18.22   0.000     1.104645    1.370975
---------------+----------------------------------------------------------------
Diff           |
    officepers |  -.4156052   .0235719   -17.63   0.000    -.4618053   -.3694052
  sectyapppers |  -.2751554   .0232981   -11.81   0.000    -.3208189    -.229492
 leaderrelatvs |  -.0043786   .0292433    -0.15   0.881    -.0616944    .0529372
  milmeritpers |   .3095569    .029577    10.47   0.000     .2515871    .3675267
    heirfamily |    .428603   .0306311    13.99   0.000     .3685671    .4886389
   paramilpers |   .5312431   .0284956    18.64   0.000     .4753928    .5870935
    milnotrial |   .5512832   .0330536    16.68   0.000     .4864993    .6160672
partyexcompers |   .8450091   .0449525    18.80   0.000     .7569039    .9331143
      heirclan |   .8836737   .0706709    12.50   0.000     .7451612    1.022186
  partyrbrstmp |   .9548353   .0501378    19.04   0.000     .8565671    1.053103
   createparty |   1.680027   .0716837    23.44   0.000     1.539529    1.820524
--------------------------------------------------------------------------------

.                 irtgraph icc (heirclan,lcolor(red)) (partyexcompers,lcolor(lime)) (p
> artyrbrstmp,lcolor(cyan)) ///
>                         (heirfam,lcolor(blue)) (leaderrel,lcolor(blue)) (sectyappper
> s,lcolor(blue)) ///
>                         (milmeritpers,lcolor(blue)) (milnotrial,lcolor(blue))  (para
> milpers,lcolor(blue)) ///
>                         (officepers,lcolor(blue))   (createparty,lcolor(blue)),legen
> d(col(3) pos(6)size(vsmall)) ///
>                         scheme(lean2) ylab(,glcolor(gs15)) xtitle({&theta})
(note: scheme lean2 not found, using s2color)

.                 graph export "$dir/golden/ICC-Personalism11.pdf", as(pdf)  replace  
>  
(file /Users/lee/Dropbox/Datavers/golden/ICC-Personalism11.pdf written in PDF format)

.                         
.                         * drop heirclan *
.                 alpha $pers10,gen(alphapers10) std item

Test scale = mean(standardized items)

                                                            average
                             item-test     item-rest       interitem
Item         |  Obs  Sign   correlation   correlation     correlation     alpha
-------------+-----------------------------------------------------------------
sectyapppers | 4591    +       0.6750        0.5627          0.2460      0.7460
officepers   | 4591    +       0.7003        0.5943          0.2419      0.7418
paramilpers  | 4591    +       0.5989        0.4699          0.2582      0.7581
milmeritpers | 4591    +       0.5878        0.4566          0.2600      0.7598
milnotrial   | 4591    +       0.5926        0.4622          0.2593      0.7590
partyexcom~s | 4591    +       0.5450        0.4058          0.2669      0.7662
createparty  | 4591    +       0.4888        0.3407          0.2760      0.7743
partyrbrstmp | 4591    +       0.5359        0.3952          0.2684      0.7675
leaderrela~s | 4591    +       0.5244        0.3818          0.2702      0.7692
heirfamily   | 4591    +       0.5418        0.4020          0.2674      0.7667
-------------+-----------------------------------------------------------------
Test scale   |                                               0.2614      0.7797
-------------------------------------------------------------------------------

.                 irt 2pl $pers10   

Fitting fixed-effects model:

Iteration 0:   log likelihood = -29231.477  
Iteration 1:   log likelihood = -29189.323  
Iteration 2:   log likelihood = -29189.307  
Iteration 3:   log likelihood = -29189.307  

Fitting full model:

Iteration 0:   log likelihood = -26413.179  
Iteration 1:   log likelihood = -25514.101  
Iteration 2:   log likelihood = -25455.696  
Iteration 3:   log likelihood = -25454.309  
Iteration 4:   log likelihood = -25454.298  
Iteration 5:   log likelihood = -25454.298  

Two-parameter logistic model                    Number of obs     =      4,591
Log likelihood = -25454.298
------------------------------------------------------------------------------
             |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
sectyapppers |
     Discrim |   2.379168   .1062988    22.38   0.000     2.170826     2.58751
        Diff |  -.2812886   .0237177   -11.86   0.000    -.3277744   -.2348027
-------------+----------------------------------------------------------------
officepers   |
     Discrim |   2.921584   .1451248    20.13   0.000     2.637144    3.206023
        Diff |  -.4074862   .0231006   -17.64   0.000    -.4527626   -.3622099
-------------+----------------------------------------------------------------
paramilpers  |
     Discrim |   1.686071   .0773052    21.81   0.000     1.534555    1.837586
        Diff |   .5405426   .0293617    18.41   0.000     .4829947    .5980905
-------------+----------------------------------------------------------------
milmeritpers |
     Discrim |   1.428898   .0619382    23.07   0.000     1.307501    1.550295
        Diff |   .3082331   .0295027    10.45   0.000     .2504088    .3660574
-------------+----------------------------------------------------------------
milnotrial   |
     Discrim |    1.44847   .0643431    22.51   0.000      1.32236     1.57458
        Diff |   .5347863   .0317598    16.84   0.000     .4725383    .5970344
-------------+----------------------------------------------------------------
partyexcom~s |
     Discrim |   1.265753   .0643627    19.67   0.000     1.139604    1.391901
        Diff |   .7907634   .0405219    19.51   0.000     .7113419    .8701849
-------------+----------------------------------------------------------------
createparty  |
     Discrim |   1.314643   .0703573    18.69   0.000     1.176745     1.45254
        Diff |   1.617947   .0660462    24.50   0.000     1.488499    1.747395
-------------+----------------------------------------------------------------
partyrbrstmp |
     Discrim |   1.224863   .0626443    19.55   0.000     1.102083    1.347644
        Diff |   .8778126   .0435834    20.14   0.000     .7923908    .9632345
-------------+----------------------------------------------------------------
leaderrela~s |
     Discrim |   1.209622   .0552927    21.88   0.000      1.10125    1.317993
        Diff |  -.0073516   .0314292    -0.23   0.815    -.0689517    .0542486
-------------+----------------------------------------------------------------
heirfamily   |
     Discrim |   1.303509    .060724    21.47   0.000     1.184492    1.422526
        Diff |   .4506883   .0327928    13.74   0.000     .3864155    .5149611
------------------------------------------------------------------------------

.                 predict irtpers10, latent
(option ebmeans assumed)
(using 7 quadrature points)

.                 estat report $pers10, byparm sort(a)

Two-parameter logistic model                    Number of obs     =      4,591
Log likelihood = -25454.298
--------------------------------------------------------------------------------
               |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
---------------+----------------------------------------------------------------
Discrim        |
 leaderrelatvs |   1.209622   .0552927    21.88   0.000      1.10125    1.317993
  partyrbrstmp |   1.224863   .0626443    19.55   0.000     1.102083    1.347644
partyexcompers |   1.265753   .0643627    19.67   0.000     1.139604    1.391901
    heirfamily |   1.303509    .060724    21.47   0.000     1.184492    1.422526
   createparty |   1.314643   .0703573    18.69   0.000     1.176745     1.45254
  milmeritpers |   1.428898   .0619382    23.07   0.000     1.307501    1.550295
    milnotrial |    1.44847   .0643431    22.51   0.000      1.32236     1.57458
   paramilpers |   1.686071   .0773052    21.81   0.000     1.534555    1.837586
  sectyapppers |   2.379168   .1062988    22.38   0.000     2.170826     2.58751
    officepers |   2.921584   .1451248    20.13   0.000     2.637144    3.206023
---------------+----------------------------------------------------------------
Diff           |
 leaderrelatvs |  -.0073516   .0314292    -0.23   0.815    -.0689517    .0542486
  partyrbrstmp |   .8778126   .0435834    20.14   0.000     .7923908    .9632345
partyexcompers |   .7907634   .0405219    19.51   0.000     .7113419    .8701849
    heirfamily |   .4506883   .0327928    13.74   0.000     .3864155    .5149611
   createparty |   1.617947   .0660462    24.50   0.000     1.488499    1.747395
  milmeritpers |   .3082331   .0295027    10.45   0.000     .2504088    .3660574
    milnotrial |   .5347863   .0317598    16.84   0.000     .4725383    .5970344
   paramilpers |   .5405426   .0293617    18.41   0.000     .4829947    .5980905
  sectyapppers |  -.2812886   .0237177   -11.86   0.000    -.3277744   -.2348027
    officepers |  -.4074862   .0231006   -17.64   0.000    -.4527626   -.3622099
--------------------------------------------------------------------------------

.                 irtgraph iif  (sectyapppers,lcolor(blue)) (milmeritpers,lcolor(red))
>  (milnotrial,lcolor(green)) ///
>                         (paramilpers,lcolor(cyan)) (officepers,lcolor(gs12)) (partye
> xcompers,lcolor(olive)) (partyrbrstmp,lcolor(sand)) ///
>                         (createparty,lcolor(ltblue)) (leaderrel,lcolor(purple)) (hei
> rfamily,lcolor(lime))  ///
>                         ,legend(col(4) pos(6)) scheme(lean2) ylab(,glcolor(gs15)) xt
> itle({&theta})
(note: scheme lean2 not found, using s2color)

.                 graph export "$dir/golden/IIF-Personalism10.pdf", as(pdf) replace
(file /Users/lee/Dropbox/Datavers/golden/IIF-Personalism10.pdf written in PDF format)

.                 qui irt 2pl $pers10   

.                 estat report $pers10, byparm sort(a)  /* leaderrels is lowest */

Two-parameter logistic model                    Number of obs     =      4,591
Log likelihood = -25454.298
--------------------------------------------------------------------------------
               |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
---------------+----------------------------------------------------------------
Discrim        |
 leaderrelatvs |   1.209622   .0552927    21.88   0.000      1.10125    1.317993
  partyrbrstmp |   1.224863   .0626443    19.55   0.000     1.102083    1.347644
partyexcompers |   1.265753   .0643627    19.67   0.000     1.139604    1.391901
    heirfamily |   1.303509    .060724    21.47   0.000     1.184492    1.422526
   createparty |   1.314643   .0703573    18.69   0.000     1.176745     1.45254
  milmeritpers |   1.428898   .0619382    23.07   0.000     1.307501    1.550295
    milnotrial |    1.44847   .0643431    22.51   0.000      1.32236     1.57458
   paramilpers |   1.686071   .0773052    21.81   0.000     1.534555    1.837586
  sectyapppers |   2.379168   .1062988    22.38   0.000     2.170826     2.58751
    officepers |   2.921584   .1451248    20.13   0.000     2.637144    3.206023
---------------+----------------------------------------------------------------
Diff           |
 leaderrelatvs |  -.0073516   .0314292    -0.23   0.815    -.0689517    .0542486
  partyrbrstmp |   .8778126   .0435834    20.14   0.000     .7923908    .9632345
partyexcompers |   .7907634   .0405219    19.51   0.000     .7113419    .8701849
    heirfamily |   .4506883   .0327928    13.74   0.000     .3864155    .5149611
   createparty |   1.617947   .0660462    24.50   0.000     1.488499    1.747395
  milmeritpers |   .3082331   .0295027    10.45   0.000     .2504088    .3660574
    milnotrial |   .5347863   .0317598    16.84   0.000     .4725383    .5970344
   paramilpers |   .5405426   .0293617    18.41   0.000     .4829947    .5980905
  sectyapppers |  -.2812886   .0237177   -11.86   0.000    -.3277744   -.2348027
    officepers |  -.4074862   .0231006   -17.64   0.000    -.4527626   -.3622099
--------------------------------------------------------------------------------

.                 qui irt 2pl $pers8  heirfam 

.                 estat report $pers8 heirfam, byparm sort(a)  /* heirfamily is lowest
>  */ 

Two-parameter logistic model                    Number of obs     =      4,591
Log likelihood = -22679.422
--------------------------------------------------------------------------------
               |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
---------------+----------------------------------------------------------------
Discrim        |
    heirfamily |    1.03041   .0518193    19.88   0.000     .9288462    1.131974
   createparty |   1.371884   .0725882    18.90   0.000     1.229614    1.514154
   paramilpers |   1.397165   .0656109    21.29   0.000      1.26857     1.52576
  milmeritpers |   1.418565   .0622668    22.78   0.000     1.296525    1.540606
  partyrbrstmp |   1.544825   .0765813    20.17   0.000     1.394728    1.694921
    milnotrial |   1.581771   .0698147    22.66   0.000     1.444936    1.718605
partyexcompers |   1.598952   .0795062    20.11   0.000     1.443123    1.754781
  sectyapppers |   2.028866   .0891796    22.75   0.000     1.854077    2.203654
    officepers |   3.087131   .1657987    18.62   0.000     2.762171     3.41209
---------------+----------------------------------------------------------------
Diff           |
    heirfamily |   .5200948   .0397615    13.08   0.000     .4421637    .5980258
   createparty |   1.579207   .0625212    25.26   0.000     1.456667    1.701746
   paramilpers |   .5928004   .0336444    17.62   0.000     .5268586    .6587423
  milmeritpers |   .3070903   .0297076    10.34   0.000     .2488645    .3653162
  partyrbrstmp |   .7692147   .0354172    21.72   0.000     .6997983    .8386312
    milnotrial |   .5096457   .0300118    16.98   0.000     .4508236    .5684678
partyexcompers |   .6948572    .033356    20.83   0.000     .6294807    .7602338
  sectyapppers |  -.3068119   .0252992   -12.13   0.000    -.3563973   -.2572265
    officepers |  -.4074417   .0227326   -17.92   0.000    -.4519968   -.3628867
--------------------------------------------------------------------------------

.                 qui gsem (PER-> $pers11), logit var(PER@1)

.                 estat ic

Akaike's information criterion and Bayesian information criterion

-----------------------------------------------------------------------------
       Model |        Obs  ll(null)  ll(model)      df         AIC        BIC
-------------+---------------------------------------------------------------
           . |      4,591         .  -28344.18      22    56732.35   56873.85
-----------------------------------------------------------------------------
               Note: N=Obs used in calculating BIC; see [R] BIC note.

.                 ** NOTE: gsem, group() not available for Stata 14 and lower; replica
> tors only have Stata 14, so I have to comment this out **
.                 *qui gsem (PER-> $pers11), logit var(PER@1) group(inherit)
.                 estat ic

Akaike's information criterion and Bayesian information criterion

-----------------------------------------------------------------------------
       Model |        Obs  ll(null)  ll(model)      df         AIC        BIC
-------------+---------------------------------------------------------------
           . |      4,591         .  -28344.18      22    56732.35   56873.85
-----------------------------------------------------------------------------
               Note: N=Obs used in calculating BIC; see [R] BIC note.

.                 qui gsem (PER-> $pers10), logit var(PER@1)

.                 estat ic

Akaike's information criterion and Bayesian information criterion

-----------------------------------------------------------------------------
       Model |        Obs  ll(null)  ll(model)      df         AIC        BIC
-------------+---------------------------------------------------------------
           . |      4,591         .   -25454.3      20     50948.6   51077.23
-----------------------------------------------------------------------------
               Note: N=Obs used in calculating BIC; see [R] BIC note.

.                 *qui gsem (PER-> $pers10), logit var(PER@1) group(inherit)
.                 estat ic

Akaike's information criterion and Bayesian information criterion

-----------------------------------------------------------------------------
       Model |        Obs  ll(null)  ll(model)      df         AIC        BIC
-------------+---------------------------------------------------------------
           . |      4,591         .   -25454.3      20     50948.6   51077.23
-----------------------------------------------------------------------------
               Note: N=Obs used in calculating BIC; see [R] BIC note.

.                 qui gsem (PER-> $pers8), logit var(PER@1)

.                 estat ic

Akaike's information criterion and Bayesian information criterion

-----------------------------------------------------------------------------
       Model |        Obs  ll(null)  ll(model)      df         AIC        BIC
-------------+---------------------------------------------------------------
           . |      4,591         .  -19904.26      16    39840.51   39943.42
-----------------------------------------------------------------------------
               Note: N=Obs used in calculating BIC; see [R] BIC note.

.                 *qui gsem (PER-> $pers8), logit var(PER@1) group(inherit)
.                 estat ic

Akaike's information criterion and Bayesian information criterion

-----------------------------------------------------------------------------
       Model |        Obs  ll(null)  ll(model)      df         AIC        BIC
-------------+---------------------------------------------------------------
           . |      4,591         .  -19904.26      16    39840.51   39943.42
-----------------------------------------------------------------------------
               Note: N=Obs used in calculating BIC; see [R] BIC note.

.                   *** Use BIC to iteratively drop 2 variables ***
.                                         qui:gsem (PER-> officepers partyrbr create p
> artyexcompers paramilpers sectyapppers milnotrial milmeritpers heirfam leaderrel), l
> ogit var(PER@1)

.                                         estat ic

Akaike's information criterion and Bayesian information criterion

-----------------------------------------------------------------------------
       Model |        Obs  ll(null)  ll(model)      df         AIC        BIC
-------------+---------------------------------------------------------------
           . |      4,591         .   -25454.3      20     50948.6   51077.23
-----------------------------------------------------------------------------
               Note: N=Obs used in calculating BIC; see [R] BIC note.

.                                         qui:gsem (PER-> officepers partyrbr create p
> artyexcompers paramilpers sectyapppers milnotrial milmeritpers heirfam), logit var(P
> ER@1)

.                                         estat ic

Akaike's information criterion and Bayesian information criterion

-----------------------------------------------------------------------------
       Model |        Obs  ll(null)  ll(model)      df         AIC        BIC
-------------+---------------------------------------------------------------
           . |      4,591         .  -22679.42      18    45394.85   45510.62
-----------------------------------------------------------------------------
               Note: N=Obs used in calculating BIC; see [R] BIC note.

.                                         qui:gsem (PER-> officepers partyrbr create p
> artyexcompers paramilpers sectyapppers milnotrial milmeritpers leaderrel), logit var
> (PER@1)

.                                         estat ic

Akaike's information criterion and Bayesian information criterion

-----------------------------------------------------------------------------
       Model |        Obs  ll(null)  ll(model)      df         AIC        BIC
-------------+---------------------------------------------------------------
           . |      4,591         .  -22798.07      18    45632.14   45747.91
-----------------------------------------------------------------------------
               Note: N=Obs used in calculating BIC; see [R] BIC note.

.                                         qui:gsem (PER-> partyrbr create partyexcompe
> rs paramilpers sectyapppers milnotrial milmeritpers heirfam leaderrel), logit var(PE
> R@1)

.                                         estat ic

Akaike's information criterion and Bayesian information criterion

-----------------------------------------------------------------------------
       Model |        Obs  ll(null)  ll(model)      df         AIC        BIC
-------------+---------------------------------------------------------------
           . |      4,591         .  -23443.22      18    46922.45   47038.22
-----------------------------------------------------------------------------
               Note: N=Obs used in calculating BIC; see [R] BIC note.

.                                         qui:gsem (PER-> officepers create partyexcom
> pers paramilpers sectyapppers milnotrial milmeritpers heirfam leaderrel), logit var(
> PER@1)

.                                         estat ic

Akaike's information criterion and Bayesian information criterion

-----------------------------------------------------------------------------
       Model |        Obs  ll(null)  ll(model)      df         AIC        BIC
-------------+---------------------------------------------------------------
           . |      4,591         .   -22970.3      18    45976.61   46092.38
-----------------------------------------------------------------------------
               Note: N=Obs used in calculating BIC; see [R] BIC note.

.                                         qui:gsem (PER-> officepers partyrbr partyexc
> ompers paramilpers sectyapppers milnotrial milmeritpers heirfam leaderrel), logit va
> r(PER@1)

.                                         estat ic

Akaike's information criterion and Bayesian information criterion

-----------------------------------------------------------------------------
       Model |        Obs  ll(null)  ll(model)      df         AIC        BIC
-------------+---------------------------------------------------------------
           . |      4,591         .  -23704.05      18    47444.11   47559.88
-----------------------------------------------------------------------------
               Note: N=Obs used in calculating BIC; see [R] BIC note.

.                                         qui:gsem (PER-> officepers partyrbr create p
> aramilpers sectyapppers milnotrial milmeritpers heirfam leaderrel), logit var(PER@1)

.                                         estat ic

Akaike's information criterion and Bayesian information criterion

-----------------------------------------------------------------------------
       Model |        Obs  ll(null)  ll(model)      df         AIC        BIC
-------------+---------------------------------------------------------------
           . |      4,591         .  -22932.95      18     45901.9   46017.67
-----------------------------------------------------------------------------
               Note: N=Obs used in calculating BIC; see [R] BIC note.

.                                         qui:gsem (PER-> officepers partyrbr create p
> artyexcompers sectyapppers milnotrial milmeritpers heirfam leaderrel), logit var(PER
> @1)

.                                         estat ic

Akaike's information criterion and Bayesian information criterion

-----------------------------------------------------------------------------
       Model |        Obs  ll(null)  ll(model)      df         AIC        BIC
-------------+---------------------------------------------------------------
           . |      4,591         .  -23024.33      18    46084.66   46200.44
-----------------------------------------------------------------------------
               Note: N=Obs used in calculating BIC; see [R] BIC note.

.                                         qui:gsem (PER-> officepers partyrbr create p
> artyexcompers paramilpers milnotrial milmeritpers heirfam leaderrel), logit var(PER@
> 1)

.                                         estat ic

Akaike's information criterion and Bayesian information criterion

-----------------------------------------------------------------------------
       Model |        Obs  ll(null)  ll(model)      df         AIC        BIC
-------------+---------------------------------------------------------------
           . |      4,591         .  -23223.36      18    46482.71   46598.49
-----------------------------------------------------------------------------
               Note: N=Obs used in calculating BIC; see [R] BIC note.

.                                         qui:gsem (PER-> officepers partyrbr create p
> artyexcompers paramilpers sectyapppers milmeritpers heirfam leaderrel), logit var(PE
> R@1)

.                                         estat ic

Akaike's information criterion and Bayesian information criterion

-----------------------------------------------------------------------------
       Model |        Obs  ll(null)  ll(model)      df         AIC        BIC
-------------+---------------------------------------------------------------
           . |      4,591         .  -22932.82      18    45901.63   46017.41
-----------------------------------------------------------------------------
               Note: N=Obs used in calculating BIC; see [R] BIC note.

.                                         qui:gsem (PER-> officepers partyrbr create p
> artyexcompers paramilpers sectyapppers milnotrial heirfam leaderrel), logit var(PER@
> 1)

.                                         estat ic

Akaike's information criterion and Bayesian information criterion

-----------------------------------------------------------------------------
       Model |        Obs  ll(null)  ll(model)      df         AIC        BIC
-------------+---------------------------------------------------------------
           . |      4,591         .  -22841.11      18    45718.21   45833.99
-----------------------------------------------------------------------------
               Note: N=Obs used in calculating BIC; see [R] BIC note.

.                                          * After dropping leaderrel *
.                                         qui:gsem (PER-> officepers partyrbr create p
> artyexcompers paramilpers sectyapppers milnotrial milmeritpers heirfam), logit var(P
> ER@1)

.                                         estat ic

Akaike's information criterion and Bayesian information criterion

-----------------------------------------------------------------------------
       Model |        Obs  ll(null)  ll(model)      df         AIC        BIC
-------------+---------------------------------------------------------------
           . |      4,591         .  -22679.42      18    45394.85   45510.62
-----------------------------------------------------------------------------
               Note: N=Obs used in calculating BIC; see [R] BIC note.

.                                         qui:gsem (PER-> officepers partyrbr create p
> artyexcompers paramilpers sectyapppers milnotrial milmeritpers ), logit var(PER@1)

.                                         estat ic

Akaike's information criterion and Bayesian information criterion

-----------------------------------------------------------------------------
       Model |        Obs  ll(null)  ll(model)      df         AIC        BIC
-------------+---------------------------------------------------------------
           . |      4,591         .  -19904.26      16    39840.51   39943.42
-----------------------------------------------------------------------------
               Note: N=Obs used in calculating BIC; see [R] BIC note.

.                                         qui:gsem (PER-> partyrbr create partyexcompe
> rs paramilpers sectyapppers milnotrial milmeritpers heirfam ), logit var(PER@1)

.                                         estat ic

Akaike's information criterion and Bayesian information criterion

-----------------------------------------------------------------------------
       Model |        Obs  ll(null)  ll(model)      df         AIC        BIC
-------------+---------------------------------------------------------------
           . |      4,591         .   -20646.6      16    41325.19    41428.1
-----------------------------------------------------------------------------
               Note: N=Obs used in calculating BIC; see [R] BIC note.

.                                         qui:gsem (PER-> officepers create partyexcom
> pers paramilpers sectyapppers milnotrial milmeritpers heirfam ), logit var(PER@1)

.                                         estat ic

Akaike's information criterion and Bayesian information criterion

-----------------------------------------------------------------------------
       Model |        Obs  ll(null)  ll(model)      df         AIC        BIC
-------------+---------------------------------------------------------------
           . |      4,591         .  -20303.48      16    40638.96   40741.87
-----------------------------------------------------------------------------
               Note: N=Obs used in calculating BIC; see [R] BIC note.

.                                         qui:gsem (PER-> officepers partyrbr partyexc
> ompers paramilpers sectyapppers milnotrial milmeritpers heirfam ), logit var(PER@1)

.                                         estat ic

Akaike's information criterion and Bayesian information criterion

-----------------------------------------------------------------------------
       Model |        Obs  ll(null)  ll(model)      df         AIC        BIC
-------------+---------------------------------------------------------------
           . |      4,591         .  -20946.58      16    41925.15   42028.06
-----------------------------------------------------------------------------
               Note: N=Obs used in calculating BIC; see [R] BIC note.

.                                         qui:gsem (PER-> officepers partyrbr create p
> aramilpers sectyapppers milnotrial milmeritpers heirfam ), logit var(PER@1)

.                                         estat ic

Akaike's information criterion and Bayesian information criterion

-----------------------------------------------------------------------------
       Model |        Obs  ll(null)  ll(model)      df         AIC        BIC
-------------+---------------------------------------------------------------
           . |      4,591         .  -20267.66      16    40567.31   40670.22
-----------------------------------------------------------------------------
               Note: N=Obs used in calculating BIC; see [R] BIC note.

.                                         qui:gsem (PER-> officepers partyrbr create p
> artyexcompers sectyapppers milnotrial milmeritpers heirfam ), logit var(PER@1)

.                                         estat ic

Akaike's information criterion and Bayesian information criterion

-----------------------------------------------------------------------------
       Model |        Obs  ll(null)  ll(model)      df         AIC        BIC
-------------+---------------------------------------------------------------
           . |      4,591         .   -20135.3      16    40302.61   40405.52
-----------------------------------------------------------------------------
               Note: N=Obs used in calculating BIC; see [R] BIC note.

.                                         qui:gsem (PER-> officepers partyrbr create p
> artyexcompers paramilpers milnotrial milmeritpers heirfam ), logit var(PER@1)

.                                         estat ic

Akaike's information criterion and Bayesian information criterion

-----------------------------------------------------------------------------
       Model |        Obs  ll(null)  ll(model)      df         AIC        BIC
-------------+---------------------------------------------------------------
           . |      4,591         .  -20314.03      16    40660.05   40762.96
-----------------------------------------------------------------------------
               Note: N=Obs used in calculating BIC; see [R] BIC note.

.                                         qui:gsem (PER-> officepers partyrbr create p
> artyexcompers paramilpers sectyapppers milmeritpers heirfam ), logit var(PER@1)

.                                         estat ic

Akaike's information criterion and Bayesian information criterion

-----------------------------------------------------------------------------
       Model |        Obs  ll(null)  ll(model)      df         AIC        BIC
-------------+---------------------------------------------------------------
           . |      4,591         .  -20212.49      16    40456.98   40559.89
-----------------------------------------------------------------------------
               Note: N=Obs used in calculating BIC; see [R] BIC note.

.                                         qui:gsem (PER-> officepers partyrbr create p
> artyexcompers paramilpers sectyapppers milnotrial heirfam ), logit var(PER@1)

.                                         estat ic

Akaike's information criterion and Bayesian information criterion

-----------------------------------------------------------------------------
       Model |        Obs  ll(null)  ll(model)      df         AIC        BIC
-------------+---------------------------------------------------------------
           . |      4,591         .  -20054.34      16    40140.68   40243.59
-----------------------------------------------------------------------------
               Note: N=Obs used in calculating BIC; see [R] BIC note.

.                 
.                         * drop leaderrel & heirfam *
.                 alpha $pers8,gen(alphapers8) std item

Test scale = mean(standardized items)

                                                            average
                             item-test     item-rest       interitem
Item         |  Obs  Sign   correlation   correlation     correlation     alpha
-------------+-----------------------------------------------------------------
sectyapppers | 4591    +       0.6413        0.4953          0.2804      0.7317
officepers   | 4591    +       0.6830        0.5485          0.2706      0.7220
paramilpers  | 4591    +       0.5528        0.3863          0.3010      0.7509
milmeritpers | 4591    +       0.6039        0.4486          0.2891      0.7400
milnotrial   | 4591    +       0.6465        0.5019          0.2792      0.7305
partyexcom~s | 4591    +       0.6334        0.4853          0.2822      0.7335
createparty  | 4591    +       0.5208        0.3481          0.3085      0.7575
partyrbrstmp | 4591    +       0.6242        0.4738          0.2844      0.7356
-------------+-----------------------------------------------------------------
Test scale   |                                               0.2869      0.7630
-------------------------------------------------------------------------------

.                 irt 2pl $pers8   

Fitting fixed-effects model:

Iteration 0:   log likelihood = -22969.605  
Iteration 1:   log likelihood =  -22931.47  
Iteration 2:   log likelihood = -22931.455  
Iteration 3:   log likelihood = -22931.455  

Fitting full model:

Iteration 0:   log likelihood = -20780.536  
Iteration 1:   log likelihood = -19957.955  
Iteration 2:   log likelihood = -19906.173  
Iteration 3:   log likelihood =  -19904.26  
Iteration 4:   log likelihood = -19904.256  
Iteration 5:   log likelihood = -19904.256  

Two-parameter logistic model                    Number of obs     =      4,591
Log likelihood = -19904.256
------------------------------------------------------------------------------
             |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
sectyapppers |
     Discrim |   1.760407   .0783432    22.47   0.000     1.606857    1.913957
        Diff |   -.332487   .0270983   -12.27   0.000    -.3855987   -.2793753
-------------+----------------------------------------------------------------
officepers   |
     Discrim |   2.890976   .1530346    18.89   0.000     2.591034    3.190918
        Diff |  -.4202195   .0232182   -18.10   0.000    -.4657263   -.3747127
-------------+----------------------------------------------------------------
paramilpers  |
     Discrim |   1.111254   .0554924    20.03   0.000     1.002491    1.220017
        Diff |   .6777748   .0411038    16.49   0.000      .597213    .7583367
-------------+----------------------------------------------------------------
milmeritpers |
     Discrim |   1.366218   .0621683    21.98   0.000     1.244371    1.488066
        Diff |   .3094894   .0305383    10.13   0.000     .2496355    .3693433
-------------+----------------------------------------------------------------
milnotrial   |
     Discrim |   1.558655    .070848    22.00   0.000     1.419795    1.697514
        Diff |   .5093505   .0305189    16.69   0.000     .4495346    .5691664
-------------+----------------------------------------------------------------
partyexcom~s |
     Discrim |   2.131928   .1134983    18.78   0.000     1.909476    2.354381
        Diff |   .6085253   .0280052    21.73   0.000     .5536361    .6634145
-------------+----------------------------------------------------------------
createparty  |
     Discrim |   1.283182   .0690587    18.58   0.000      1.14783    1.418535
        Diff |   1.644761   .0679592    24.20   0.000     1.511564    1.777959
-------------+----------------------------------------------------------------
partyrbrstmp |
     Discrim |   2.004324   .1048153    19.12   0.000      1.79889    2.209759
        Diff |   .6785336   .0298494    22.73   0.000     .6200299    .7370373
------------------------------------------------------------------------------

.                 predict irtpers8, latent se(se_irtpers8)
(option ebmeans assumed)
(using 7 quadrature points)

.                 estat report $pers8, byparm sort(b)

Two-parameter logistic model                    Number of obs     =      4,591
Log likelihood = -19904.256
--------------------------------------------------------------------------------
               |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
---------------+----------------------------------------------------------------
Discrim        |
    officepers |   2.890976   .1530346    18.89   0.000     2.591034    3.190918
  sectyapppers |   1.760407   .0783432    22.47   0.000     1.606857    1.913957
  milmeritpers |   1.366218   .0621683    21.98   0.000     1.244371    1.488066
    milnotrial |   1.558655    .070848    22.00   0.000     1.419795    1.697514
partyexcompers |   2.131928   .1134983    18.78   0.000     1.909476    2.354381
   paramilpers |   1.111254   .0554924    20.03   0.000     1.002491    1.220017
  partyrbrstmp |   2.004324   .1048153    19.12   0.000      1.79889    2.209759
   createparty |   1.283182   .0690587    18.58   0.000      1.14783    1.418535
---------------+----------------------------------------------------------------
Diff           |
    officepers |  -.4202195   .0232182   -18.10   0.000    -.4657263   -.3747127
  sectyapppers |   -.332487   .0270983   -12.27   0.000    -.3855987   -.2793753
  milmeritpers |   .3094894   .0305383    10.13   0.000     .2496355    .3693433
    milnotrial |   .5093505   .0305189    16.69   0.000     .4495346    .5691664
partyexcompers |   .6085253   .0280052    21.73   0.000     .5536361    .6634145
   paramilpers |   .6777748   .0411038    16.49   0.000      .597213    .7583367
  partyrbrstmp |   .6785336   .0298494    22.73   0.000     .6200299    .7370373
   createparty |   1.644761   .0679592    24.20   0.000     1.511564    1.777959
--------------------------------------------------------------------------------

.                 irtgraph iif  (sectyapppers,lcolor(blue)) (milmeritpers,lcolor(red))
>  (milnotrial,lcolor(green)) ///
>                         (paramilpers,lcolor(cyan)) (officepers,lcolor(gs12)) (partye
> xcompers,lcolor(olive)) (partyrbrstmp,lcolor(sand)) ///
>                         (createparty,lcolor(ltblue)),legend(col(4) pos(6)) scheme(le
> an2) ylab(,glcolor(gs15)) xtitle({&theta})
(note: scheme lean2 not found, using s2color)

.                 graph export "$dir/golden/IIF-Personalism8.pdf", as(pdf) replace
(file /Users/lee/Dropbox/Datavers/golden/IIF-Personalism8.pdf written in PDF format)

.                 
.                 ***  full info 10 item, mix 2pl + grm latent variable ***
.                 irt (2pl sectyapppers officepers heirfamily paramilpers leaderrel mi
> lnotrial partyexcompers ///
>                         createparty partyrbr) (grm milmerit_pers),vce(cluster gwf_le
> aderid)

Fitting fixed-effects model:

Iteration 0:   log likelihood = -31019.427  
Iteration 1:   log likelihood = -30979.552  
Iteration 2:   log likelihood = -30979.535  
Iteration 3:   log likelihood = -30979.535  

Fitting full model:

Iteration 0:   log pseudolikelihood = -28080.434  
Iteration 1:   log pseudolikelihood = -27193.373  
Iteration 2:   log pseudolikelihood =   -27141.6  
Iteration 3:   log pseudolikelihood =   -27140.7  
Iteration 4:   log pseudolikelihood =   -27140.7  

Hybrid IRT model                                Number of obs     =      4,591
Log pseudolikelihood =   -27140.7
                         (Std. Err. adjusted for 526 clusters in gwf_leaderid)
------------------------------------------------------------------------------
             |               Robust
             |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
------------------------------------------------------------------------------
2pl         
------------------------------------------------------------------------------
sectyapppers |
     Discrim |   2.501004   .3862215     6.48   0.000     1.744024    3.257984
        Diff |  -.2804932   .0823858    -3.40   0.001    -.4419665   -.1190199
-------------+----------------------------------------------------------------
officepers   |
     Discrim |   2.589952   .5043104     5.14   0.000     1.601522    3.578382
        Diff |  -.4296412   .0846676    -5.07   0.000    -.5955866   -.2636958
-------------+----------------------------------------------------------------
heirfamily   |
     Discrim |   1.355524   .2779914     4.88   0.000     .8106711    1.900377
        Diff |   .4393618   .1435147     3.06   0.002     .1580781    .7206456
-------------+----------------------------------------------------------------
paramilpers  |
     Discrim |   1.743129   .3548643     4.91   0.000     1.047608     2.43865
        Diff |   .5312401    .127576     4.16   0.000     .2811959    .7812844
-------------+----------------------------------------------------------------
leaderrela~s |
     Discrim |   1.266388    .244565     5.18   0.000     .7870489    1.745726
        Diff |  -.0087947   .1156204    -0.08   0.939    -.2354065    .2178171
-------------+----------------------------------------------------------------
milnotrial   |
     Discrim |   1.461591   .2329731     6.27   0.000     1.004972     1.91821
        Diff |   .5308672   .1345352     3.95   0.000      .267183    .7945514
-------------+----------------------------------------------------------------
partyexcom~s |
     Discrim |   1.184176   .3256366     3.64   0.000     .5459404    1.822413
        Diff |   .8227716    .208066     3.95   0.000     .4149698    1.230573
-------------+----------------------------------------------------------------
createparty  |
     Discrim |   1.317741   .2560479     5.15   0.000     .8158967    1.819586
        Diff |   1.616138   .2848681     5.67   0.000     1.057807    2.174469
-------------+----------------------------------------------------------------
partyrbrstmp |
     Discrim |   1.137854   .2943561     3.87   0.000     .5609271    1.714782
        Diff |   .9187907   .2281162     4.03   0.000     .4716911     1.36589
------------------------------------------------------------------------------
grm         
------------------------------------------------------------------------------
milmerit_p~s |
     Discrim |   1.350585     .16013     8.43   0.000     1.036736    1.664434
        Diff |
        >=1  |  -1.160247    .144906    -8.01   0.000    -1.444257   -.8762364
         =2  |   .3178499   .1246042     2.55   0.011     .0736302    .5620696
------------------------------------------------------------------------------

.                 estat report  sectyapppers officepers heirfamily paramilpers leaderr
> el milnotrial partyexcompers ///
>                         createparty partyrbr milmerit_pers, byparm sort(b)

Hybrid IRT model                                Number of obs     =      4,591
Log pseudolikelihood =   -27140.7
                              (Std. Err. adjusted for 526 clusters in gwf_leaderid)
-----------------------------------------------------------------------------------
                  |               Robust
                  |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
------------------+----------------------------------------------------------------
             Diff |
milmerit_pers>=1  |  -1.160247    .144906    -8.01   0.000    -1.444257   -.8762364
    officepers=1  |  -.4296412   .0846676    -5.07   0.000    -.5955866   -.2636958
  sectyapppers=1  |  -.2804932   .0823858    -3.40   0.001    -.4419665   -.1190199
 leaderrelatvs=1  |  -.0087947   .1156204    -0.08   0.939    -.2354065    .2178171
 milmerit_pers=2  |   .3178499   .1246042     2.55   0.011     .0736302    .5620696
    heirfamily=1  |   .4393618   .1435147     3.06   0.002     .1580781    .7206456
    milnotrial=1  |   .5308672   .1345352     3.95   0.000      .267183    .7945514
   paramilpers=1  |   .5312401    .127576     4.16   0.000     .2811959    .7812844
partyexcompers=1  |   .8227716    .208066     3.95   0.000     .4149698    1.230573
  partyrbrstmp=1  |   .9187907   .2281162     4.03   0.000     .4716911     1.36589
   createparty=1  |   1.616138   .2848681     5.67   0.000     1.057807    2.174469
-----------------------------------------------------------------------------------

.                 irtgraph iif  (sectyapppers,lcolor(blue)) (milmerit_pers,lcolor(red)
> ) (milnotrial,lcolor(green)) ///
>                         (paramilpers,lcolor(cyan)) (officepers,lcolor(gs12)) (partye
> xcompers,lcolor(olive)) (partyrbrstmp,lcolor(sand)) ///
>                         (createparty,lcolor(ltblue)) (leaderrel,lcolor(purple)) (hei
> rfamily,lcolor(lime))  ///
>                         ,legend(col(4) pos(6)) scheme(lean2) ylab(,glcolor(gs15)) xt
> itle({&theta})
(note: scheme lean2 not found, using s2color)

.                 graph export "$dir/golden/IIF-PersonalismGRM.pdf", as(pdf) replace
(file /Users/lee/Dropbox/Datavers/golden/IIF-PersonalismGRM.pdf written in PDF format)

.                 predict irtgrm, latent
(option ebmeans assumed)
(using 7 quadrature points)

.                 spearman irtgrm irtpers*
(obs=4591)

             |   irtgrm irtpe~11 irtpe~10 irtpers8
-------------+------------------------------------
      irtgrm |   1.0000 
   irtpers11 |   0.9942   1.0000 
   irtpers10 |   0.9948   0.9944   1.0000 
    irtpers8 |   0.9257   0.9180   0.9417   1.0000 

.                 
.                 *** Look at some sample homo assumptions ***
.                 qui:irt 2pl $pers8 if inherit==1

.                 estat report,byparm sort(b)

Two-parameter logistic model                    Number of obs     =      2,381
Log likelihood = -9692.6724
--------------------------------------------------------------------------------
               |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
---------------+----------------------------------------------------------------
Discrim        |
    officepers |    3.57301   .3138942    11.38   0.000     2.957788    4.188231
  sectyapppers |   1.397346   .0853337    16.38   0.000     1.230096    1.564597
partyexcompers |   2.737215   .1977307    13.84   0.000      2.34967     3.12476
  milmeritpers |    .941565     .06705    14.04   0.000     .8101495    1.072981
  partyrbrstmp |   2.781485    .210389    13.22   0.000      2.36913     3.19384
    milnotrial |   .9169591   .0681632    13.45   0.000     .7833617    1.050556
   paramilpers |    1.08695   .0804834    13.51   0.000     .9292051    1.244694
   createparty |   .2420289   .1950768     1.24   0.215    -.1403146    .6243724
---------------+----------------------------------------------------------------
Diff           |
    officepers |  -.3146252   .0297424   -10.58   0.000    -.3729192   -.2563312
  sectyapppers |  -.1906457   .0408779    -4.66   0.000     -.270765   -.1105264
partyexcompers |   .2901552   .0309374     9.38   0.000     .2295191    .3507913
  milmeritpers |   .4164835   .0562614     7.40   0.000     .3062133    .5267537
  partyrbrstmp |   .4755404   .0327055    14.54   0.000     .4114388     .539642
    milnotrial |   .8556114   .0723056    11.83   0.000      .713895    .9973277
   paramilpers |     1.0952   .0749969    14.60   0.000      .948209    1.242191
   createparty |   17.49423   13.92827     1.26   0.209    -9.804681    44.79314
--------------------------------------------------------------------------------

.                 qui:predict irtinh1 if e(sample)==1, latent

.                 qui:irt 2pl $pers8 if inherit==0

.                 estat report,byparm sort(b)

Two-parameter logistic model                    Number of obs     =      2,210
Log likelihood = -9223.2784
--------------------------------------------------------------------------------
               |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
---------------+----------------------------------------------------------------
Discrim        |
    officepers |   2.417232   .1672359    14.45   0.000     2.089455    2.745008
  sectyapppers |   1.812127   .1168158    15.51   0.000     1.583172    2.041082
  milmeritpers |   1.536634   .0935073    16.43   0.000     1.353363    1.719905
   paramilpers |   .9448227   .0672904    14.04   0.000     .8129358     1.07671
    milnotrial |   2.246486    .137716    16.31   0.000     1.976568    2.516405
   createparty |   2.151983   .1315281    16.36   0.000     1.894192    2.409773
  partyrbrstmp |   2.546466    .186067    13.69   0.000     2.181782    2.911151
partyexcompers |   4.200284   .4472507     9.39   0.000     3.323689     5.07688
---------------+----------------------------------------------------------------
Diff           |
    officepers |  -.5435002   .0368427   -14.75   0.000    -.6157106   -.4712897
  sectyapppers |  -.5082191   .0408784   -12.43   0.000    -.5883394   -.4280989
  milmeritpers |   .2674672    .040612     6.59   0.000     .1878691    .3470653
   paramilpers |   .3131052   .0561934     5.57   0.000     .2029681    .4232423
    milnotrial |   .3350077   .0347431     9.64   0.000     .2669124     .403103
   createparty |   .5886728   .0386188    15.24   0.000     .5129814    .6643643
  partyrbrstmp |   .7741752   .0407329    19.01   0.000     .6943401    .8540102
partyexcompers |   .8062731   .0364535    22.12   0.000     .7348256    .8777206
--------------------------------------------------------------------------------

.                 qui:predict irtinh0 if e(sample)==1, latent     

.                 qui:gen irtinh = irtinh1 

.                 qui:replace irtinh=irtinh0 if irtinh==.

.                 spearman irtinh irtpers8 irtgrm
(obs=4591)

             |   irtinh irtpers8   irtgrm
-------------+---------------------------
      irtinh |   1.0000 
    irtpers8 |   0.9857   1.0000 
      irtgrm |   0.8853   0.9257   1.0000 

.                 qui:drop irtinh*

.                 
.                 qui:irt 2pl $pers8 if SeizCoup==1

.                 estat report,byparm sort(b)

Two-parameter logistic model                    Number of obs     =      1,392
Log likelihood = -6105.0464
--------------------------------------------------------------------------------
               |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
---------------+----------------------------------------------------------------
Discrim        |
    officepers |     3.0564   .2632819    11.61   0.000     2.540377    3.572423
  sectyapppers |   2.393572   .1877047    12.75   0.000     2.025678    2.761467
  milmeritpers |   1.530433    .113778    13.45   0.000     1.307433    1.753434
    milnotrial |   2.048627   .1523767    13.44   0.000     1.749975     2.34728
partyexcompers |     2.8653   .2653239    10.80   0.000     2.345275    3.385326
  partyrbrstmp |   1.939675   .1532522    12.66   0.000     1.639307    2.240044
   paramilpers |   1.249662    .102416    12.20   0.000      1.04893    1.450394
   createparty |   1.303719   .1042384    12.51   0.000     1.099416    1.508023
---------------+----------------------------------------------------------------
Diff           |
    officepers |  -.4326112   .0411003   -10.53   0.000    -.5131663    -.352056
  sectyapppers |  -.2862016   .0425318    -6.73   0.000    -.3695625   -.2028408
  milmeritpers |  -.1466048   .0503929    -2.91   0.004    -.2453732   -.0478365
    milnotrial |  -.1351879   .0438395    -3.08   0.002    -.2211118   -.0492641
partyexcompers |   .4536635   .0418951    10.83   0.000     .3715507    .5357764
  partyrbrstmp |   .5293398   .0497464    10.64   0.000     .4318387    .6268408
   paramilpers |   .5970013   .0649069     9.20   0.000     .4697861    .7242165
   createparty |   .6963643   .0661138    10.53   0.000     .5667836    .8259449
--------------------------------------------------------------------------------

.                 qui:predict irtinh1 if e(sample)==1, latent

.                 qui:irt 2pl $pers8 if SeizCoup==0

.                 estat report,byparm sort(b)

Two-parameter logistic model                    Number of obs     =      3,199
Log likelihood = -13399.535
--------------------------------------------------------------------------------
               |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
---------------+----------------------------------------------------------------
Discrim        |
    officepers |   3.135866    .235151    13.34   0.000     2.674979    3.596754
  sectyapppers |   1.484956   .0847052    17.53   0.000     1.318937    1.650975
  milmeritpers |   1.164518   .0699352    16.65   0.000     1.027448    1.301589
partyexcompers |    2.06832   .1523231    13.58   0.000     1.769773    2.366868
  partyrbrstmp |   2.363669    .188232    12.56   0.000     1.994741    2.732597
   paramilpers |   .9983298   .0688907    14.49   0.000     .8633066    1.133353
    milnotrial |   1.234937   .0760662    16.24   0.000      1.08585    1.384024
   createparty |   1.161428   .0991075    11.72   0.000     .9671809    1.355675
---------------+----------------------------------------------------------------
Diff           |
    officepers |   -.400365   .0274032   -14.61   0.000    -.4540743   -.3466558
  sectyapppers |  -.3637665   .0360807   -10.08   0.000    -.4344835   -.2930496
  milmeritpers |   .5735964    .045371    12.64   0.000     .4846709    .6625218
partyexcompers |   .6696455   .0363346    18.43   0.000     .5984311    .7408599
  partyrbrstmp |   .7081478   .0353196    20.05   0.000     .6389227     .777373
   paramilpers |   .7473851   .0573212    13.04   0.000     .6350376    .8597326
    milnotrial |   .9466984   .0544895    17.37   0.000      .839901    1.053496
   createparty |   2.463239   .1578568    15.60   0.000     2.153845    2.772633
--------------------------------------------------------------------------------

.                 qui:predict irtinh0 if e(sample)==1, latent     

.                 qui:gen irtinh = irtinh1 

.                 qui:replace irtinh=irtinh0 if irtinh==.

.                 spearman irtinh irtpers8 irtgrm
(obs=4591)

             |   irtinh irtpers8   irtgrm
-------------+---------------------------
      irtinh |   1.0000 
    irtpers8 |   0.9866   1.0000 
      irtgrm |   0.9085   0.9257   1.0000 

.                 qui:drop irtinh*

.                 
.                 qui:irt 2pl $pers8 if year>1989

.                 estat report,byparm sort(b)

Two-parameter logistic model                    Number of obs     =      1,387
Log likelihood = -6054.2069
--------------------------------------------------------------------------------
               |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
---------------+----------------------------------------------------------------
Discrim        |
  sectyapppers |   1.879478   .1790243    10.50   0.000     1.528597    2.230359
    officepers |   2.788652   .3194963     8.73   0.000     2.162451    3.414853
   paramilpers |   1.287923   .1227295    10.49   0.000     1.047377    1.528468
  milmeritpers |   1.351355   .1146118    11.79   0.000      1.12672     1.57599
    milnotrial |     1.5555   .1369019    11.36   0.000     1.287177    1.823822
partyexcompers |     1.4767    .156192     9.45   0.000     1.170569     1.78283
  partyrbrstmp |   1.472797   .1502408     9.80   0.000      1.17833    1.767263
   createparty |   1.538768   .1480594    10.39   0.000     1.248577    1.828959
---------------+----------------------------------------------------------------
Diff           |
  sectyapppers |  -.9340833   .0658753   -14.18   0.000    -1.063196   -.8049701
    officepers |  -.6574481   .0486386   -13.52   0.000    -.7527779   -.5621183
   paramilpers |    .167764   .0560817     2.99   0.003      .057846     .277682
  milmeritpers |   .1915386   .0545732     3.51   0.000     .0845771    .2985001
    milnotrial |   .3402485   .0529449     6.43   0.000     .2364783    .4440186
partyexcompers |   .7065251   .0686907    10.29   0.000     .5718938    .8411564
  partyrbrstmp |   .8958564   .0764546    11.72   0.000     .7460081    1.045705
   createparty |   1.223029   .0879475    13.91   0.000     1.050655    1.395403
--------------------------------------------------------------------------------

.                 qui:predict irtinh1 if e(sample)==1, latent

.                 qui:irt 2pl $pers8 if year<=1989

.                 estat report,byparm sort(b)

Two-parameter logistic model                    Number of obs     =      3,204
Log likelihood = -13653.726
--------------------------------------------------------------------------------
               |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
---------------+----------------------------------------------------------------
Discrim        |
    officepers |   2.760709   .1646517    16.77   0.000     2.437997     3.08342
  sectyapppers |   1.735387    .089755    19.33   0.000      1.55947    1.911303
  milmeritpers |   1.327155   .0726495    18.27   0.000     1.184765    1.469546
partyexcompers |   2.532771   .1638598    15.46   0.000     2.211612     2.85393
    milnotrial |   1.547874   .0826756    18.72   0.000     1.385833    1.709915
  partyrbrstmp |   2.381554   .1504591    15.83   0.000      2.08666    2.676448
   paramilpers |   1.043408   .0637737    16.36   0.000     .9184141    1.168402
   createparty |    1.22715   .0819434    14.98   0.000     1.066544    1.387756
---------------+----------------------------------------------------------------
Diff           |
    officepers |  -.3314441   .0273106   -12.14   0.000    -.3849719   -.2779164
  sectyapppers |  -.0884366   .0310314    -2.85   0.004     -.149257   -.0276162
  milmeritpers |    .365414      .0378     9.67   0.000     .2913272    .4395007
partyexcompers |   .5786446   .0305536    18.94   0.000     .5187607    .6385285
    milnotrial |   .5845232   .0376709    15.52   0.000     .5106895    .6583568
  partyrbrstmp |   .5997528   .0315808    18.99   0.000     .5378556      .66165
   paramilpers |   .9449054   .0595774    15.86   0.000     .8281359    1.061675
   createparty |   1.835215   .0940728    19.51   0.000     1.650836    2.019595
--------------------------------------------------------------------------------

.                 qui:predict irtinh0 if e(sample)==1, latent     

.                 qui:gen irtinh = irtinh1 

.                 qui:replace irtinh=irtinh0 if irtinh==.

.                 spearman irtinh irtpers8 irtgrm
(obs=4591)

             |   irtinh irtpers8   irtgrm
-------------+---------------------------
      irtinh |   1.0000 
    irtpers8 |   0.9893   1.0000 
      irtgrm |   0.9180   0.9257   1.0000 

.                 qui:drop irtinh*

.         
.                 * Some 2-way comparisons *
.                 twoway scatter irtgrm irtpers8, ytitle("10-item 2PL-GRM") xtitle("8-
> item 2PL") ylab(,glcol(gs16))

.                 twoway scatter pr3 irtpers8, ytitle("EFA personalism factor") xtitle
> ("8-item 2PL") ylab(,glcol(gs16))

.         
.                 * correlations among  latent dimensions*
.                 matrix m = J(6,6,.)

.                 matrix list m

symmetric m[6,6]
    c1  c2  c3  c4  c5  c6
r1   .
r2   .   .
r3   .   .   .
r4   .   .   .   .
r5   .   .   .   .   .
r6   .   .   .   .   .   .

.                 local dimensions = "Personalist persrat_1a pr3 irtpers11 irtpers10 i
> rtpers8"

.                 local i=1

.                 foreach t of local dimensions {
  2.                         local j = 1
  3.                         local klass = "Personalist persrat_1a pr3 irtpers11 irtpe
> rs10 irtpers8"
  4.                         foreach k of local klass {
  5.                                 spearman `t' `k'
  6.                                 matrix j = r(rho)
  7.                                 mat list j
  8.                                 local s = round(j[1,1],.01)
  9.                                 matrix m[`i',`j'] =`s'
 10.                                 local j= `j' + 1
 11.                         }
 12.                         local i = `i'+1
 13.                 }

 Number of obs =    4591
Spearman's rho =       1.0000

Test of Ho: Personalist and Personalist are independent
    Prob > |t| =            .

symmetric j[1,1]
    c1
r1   1

 Number of obs =    2681
Spearman's rho =       0.5568

Test of Ho: Personalist and persrat_1a are independent
    Prob > |t| =       0.0000

symmetric j[1,1]
           c1
r1  .55679904

 Number of obs =    4591
Spearman's rho =       0.4083

Test of Ho: Personalist and pr3 are independent
    Prob > |t| =       0.0000

symmetric j[1,1]
           c1
r1  .40830161

 Number of obs =    4591
Spearman's rho =       0.4088

Test of Ho: Personalist and irtpers11 are independent
    Prob > |t| =       0.0000

symmetric j[1,1]
           c1
r1  .40879591

 Number of obs =    4591
Spearman's rho =       0.4269

Test of Ho: Personalist and irtpers10 are independent
    Prob > |t| =       0.0000

symmetric j[1,1]
           c1
r1  .42692436

 Number of obs =    4591
Spearman's rho =       0.3876

Test of Ho: Personalist and irtpers8 are independent
    Prob > |t| =       0.0000

symmetric j[1,1]
           c1
r1  .38759898

 Number of obs =    2681
Spearman's rho =       0.5568

Test of Ho: persrat_1a and Personalist are independent
    Prob > |t| =       0.0000

symmetric j[1,1]
           c1
r1  .55679904

 Number of obs =    2681
Spearman's rho =       1.0000

Test of Ho: persrat_1a and persrat_1a are independent
    Prob > |t| =            .

symmetric j[1,1]
    c1
r1   1

 Number of obs =    2681
Spearman's rho =       0.6403

Test of Ho: persrat_1a and pr3 are independent
    Prob > |t| =       0.0000

symmetric j[1,1]
           c1
r1  .64025769

 Number of obs =    2681
Spearman's rho =       0.6307

Test of Ho: persrat_1a and irtpers11 are independent
    Prob > |t| =       0.0000

symmetric j[1,1]
           c1
r1  .63067483

 Number of obs =    2681
Spearman's rho =       0.6307

Test of Ho: persrat_1a and irtpers10 are independent
    Prob > |t| =       0.0000

symmetric j[1,1]
           c1
r1  .63074314

 Number of obs =    2681
Spearman's rho =       0.5686

Test of Ho: persrat_1a and irtpers8 are independent
    Prob > |t| =       0.0000

symmetric j[1,1]
           c1
r1  .56863661

 Number of obs =    4591
Spearman's rho =       0.4083

Test of Ho: pr3 and Personalist are independent
    Prob > |t| =       0.0000

symmetric j[1,1]
           c1
r1  .40830161

 Number of obs =    2681
Spearman's rho =       0.6403

Test of Ho: pr3 and persrat_1a are independent
    Prob > |t| =       0.0000

symmetric j[1,1]
           c1
r1  .64025769

 Number of obs =    4591
Spearman's rho =       1.0000

Test of Ho: pr3 and pr3 are independent
    Prob > |t| =       0.0000

symmetric j[1,1]
    c1
r1   1

 Number of obs =    4591
Spearman's rho =       0.9114

Test of Ho: pr3 and irtpers11 are independent
    Prob > |t| =       0.0000

symmetric j[1,1]
           c1
r1  .91143703

 Number of obs =    4591
Spearman's rho =       0.8966

Test of Ho: pr3 and irtpers10 are independent
    Prob > |t| =       0.0000

symmetric j[1,1]
           c1
r1  .89662839

 Number of obs =    4591
Spearman's rho =       0.7946

Test of Ho: pr3 and irtpers8 are independent
    Prob > |t| =       0.0000

symmetric j[1,1]
          c1
r1  .7946464

 Number of obs =    4591
Spearman's rho =       0.4088

Test of Ho: irtpers11 and Personalist are independent
    Prob > |t| =       0.0000

symmetric j[1,1]
           c1
r1  .40879591

 Number of obs =    2681
Spearman's rho =       0.6307

Test of Ho: irtpers11 and persrat_1a are independent
    Prob > |t| =       0.0000

symmetric j[1,1]
           c1
r1  .63067483

 Number of obs =    4591
Spearman's rho =       0.9114

Test of Ho: irtpers11 and pr3 are independent
    Prob > |t| =       0.0000

symmetric j[1,1]
           c1
r1  .91143703

 Number of obs =    4591
Spearman's rho =       1.0000

Test of Ho: irtpers11 and irtpers11 are independent
    Prob > |t| =       0.0000

symmetric j[1,1]
    c1
r1   1

 Number of obs =    4591
Spearman's rho =       0.9944

Test of Ho: irtpers11 and irtpers10 are independent
    Prob > |t| =       0.0000

symmetric j[1,1]
           c1
r1  .99442536

 Number of obs =    4591
Spearman's rho =       0.9180

Test of Ho: irtpers11 and irtpers8 are independent
    Prob > |t| =       0.0000

symmetric j[1,1]
           c1
r1  .91800526

 Number of obs =    4591
Spearman's rho =       0.4269

Test of Ho: irtpers10 and Personalist are independent
    Prob > |t| =       0.0000

symmetric j[1,1]
           c1
r1  .42692436

 Number of obs =    2681
Spearman's rho =       0.6307

Test of Ho: irtpers10 and persrat_1a are independent
    Prob > |t| =       0.0000

symmetric j[1,1]
           c1
r1  .63074314

 Number of obs =    4591
Spearman's rho =       0.8966

Test of Ho: irtpers10 and pr3 are independent
    Prob > |t| =       0.0000

symmetric j[1,1]
           c1
r1  .89662839

 Number of obs =    4591
Spearman's rho =       0.9944

Test of Ho: irtpers10 and irtpers11 are independent
    Prob > |t| =       0.0000

symmetric j[1,1]
           c1
r1  .99442536

 Number of obs =    4591
Spearman's rho =       1.0000

Test of Ho: irtpers10 and irtpers10 are independent
    Prob > |t| =       0.0000

symmetric j[1,1]
    c1
r1   1

 Number of obs =    4591
Spearman's rho =       0.9417

Test of Ho: irtpers10 and irtpers8 are independent
    Prob > |t| =       0.0000

symmetric j[1,1]
           c1
r1  .94170145

 Number of obs =    4591
Spearman's rho =       0.3876

Test of Ho: irtpers8 and Personalist are independent
    Prob > |t| =       0.0000

symmetric j[1,1]
           c1
r1  .38759898

 Number of obs =    2681
Spearman's rho =       0.5686

Test of Ho: irtpers8 and persrat_1a are independent
    Prob > |t| =       0.0000

symmetric j[1,1]
           c1
r1  .56863661

 Number of obs =    4591
Spearman's rho =       0.7946

Test of Ho: irtpers8 and pr3 are independent
    Prob > |t| =       0.0000

symmetric j[1,1]
          c1
r1  .7946464

 Number of obs =    4591
Spearman's rho =       0.9180

Test of Ho: irtpers8 and irtpers11 are independent
    Prob > |t| =       0.0000

symmetric j[1,1]
           c1
r1  .91800526

 Number of obs =    4591
Spearman's rho =       0.9417

Test of Ho: irtpers8 and irtpers10 are independent
    Prob > |t| =       0.0000

symmetric j[1,1]
           c1
r1  .94170145

 Number of obs =    4591
Spearman's rho =       1.0000

Test of Ho: irtpers8 and irtpers8 are independent
    Prob > |t| =            .

symmetric j[1,1]
    c1
r1   1

.                 matrix list m

symmetric m[6,6]
     c1   c2   c3   c4   c5   c6
r1    1
r2  .56    1
r3  .41  .64    1
r4  .41  .63  .91    1
r5  .43  .63   .9  .99    1
r6  .39  .57  .79  .92  .94    1

.                 plotmatrix, m(m) c(yellow) legend(off) title(Correlation matrix for 
> measures of Personalism,size(medium)) freq  split(0(.01)1)  xsize(3) ysize(2) /*
>                 */ xlab(1 "Categorical-GWF" 2 "Ordinal-Weeks" 3 "Factor"  4 "11-item
>  2PL" 5 "10-item 2PL" 6 "8-item 2PL",labsize(vsmall))/*
>                 */ ylab(0 "Categorical-GWF" -1 "Ordinal-Weeks" -2 "Factor" -3 "11-it
> em 2PL" -4 "10-item 2PL" -5 "8-item 2PL",labsize(vsmall))  
WARNING: Tested only for Stata version 15.1 and higher.
Your Stata version 14.2 is not officially supported.
SPLIT 0 .01 .02 .03 .04 .05 .06 .07 .08 .09 .1 .11 .12 .13 .14 .15 .16 .17 .18 .19 .2 
> .21 .22 .23 .24 .25 .26 .27 .28 .29 .3 .31 .32 .33 .34 .35 .36 .37 .38 .39 .4 .41 .4
> 2 .43 .44 .45 .46 .47 .48 .49 .5 .51 .52 .53 .54 .55 .56 .57 .58 .59 .6 .61 .62 .63 
> .64 .65 .66 .67 .68 .69 .7 .71 .72 .73 .74 .75 .76 .77 .78 .79 .8 .81 .82 .83 .84 .8
> 5 .86 .87 .88 .89 .9 .91 .92 .93 .94 .95 .96 .97 .98 .99 1

.                 graph export "$dir/golden/ICorrMatrix-Personalism.pdf", as(pdf) repl
> ace
(file /Users/lee/Dropbox/Datavers/golden/ICorrMatrix-Personalism.pdf written in PDF fo
> rmat)

. 
.                 
.                   * Show latent dimensions for China * 
.                         sum persrat_1a pr3 irtpers8

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
  persrat_1a |      2,681    .5111517    .4102033          0          1
         pr3 |      4,591    7.64e-10     .982823  -2.265968    2.35445
    irtpers8 |      4,591    .0014422    .8725584  -1.321256   1.828571

.                         local var = "irtpers8 pr3"

.                         foreach v of local var {
  2.                                 qui: sum `v'
  3.                                 gen x`v' = (`v' + abs(r(min)))/ (abs(r(min)) + r(
> max))
  4.                         } 

.                          sum persrat_1a xpr3 xirtpers8

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
  persrat_1a |      2,681    .5111517    .4102033          0          1
        xpr3 |      4,591    .4904249     .212713          0          1
   xirtpers8 |      4,591    .4199273    .2770179          0          1

.                          twoway (line xpr3 xirtpers8 persrat_1a year if cow==710 & y
> ear>1948 & year<2011,sort lcolor(blue green red) yscale(range(0 1)) ylab(0 (.2) 1,gl
> col(gs15)) ///
>                                                         xscale(range (1950 2010)) xl
> abel(1950 (10) 2010) title("Personalism measures, China 1949-2010") ///
>                                                         ytitle("Personalism score") 
> xtitle("Year",height(6))  legend(lab(1 "Factor") lab(2 "8-item IRT-2PL")  ///
>                                                         lab(3 "Weeks") pos(6) col(3)
>  ring(2)))  

.                         graph export "$dir/golden/China-Latent-Pers.pdf", as(pdf) re
> place
(file /Users/lee/Dropbox/Datavers/golden/China-Latent-Pers.pdf written in PDF format)

.         
.                 spearman pr1 pr2 pr3
(obs=4591)

             |      pr1      pr2      pr3
-------------+---------------------------
         pr1 |   1.0000 
         pr2 |  -0.2335   1.0000 
         pr3 |  -0.1711   0.0571   1.0000 

.                 spearman pr3 alphapers11 irtpers11 alphapers10 irtpers10 alphapers8 
> irtpers8 irtgrm
(obs=4591)

             |      pr3 alpha~11 irtpe~11 alpha~10 irtpe~10 alphap~8 irtpers8
-------------+---------------------------------------------------------------
         pr3 |   1.0000 
 alphapers11 |   0.9100   1.0000 
   irtpers11 |   0.9114   0.9835   1.0000 
 alphapers10 |   0.8868   0.9859   0.9764   1.0000 
   irtpers10 |   0.8966   0.9782   0.9944   0.9857   1.0000 
  alphapers8 |   0.8072   0.9398   0.9185   0.9693   0.9428   1.0000 
    irtpers8 |   0.7946   0.9299   0.9180   0.9570   0.9417   0.9862   1.0000 
      irtgrm |   0.9009   0.9768   0.9942   0.9807   0.9948   0.9296   0.9257 

             |   irtgrm
-------------+---------
      irtgrm |   1.0000 

.                 global d1 = "pr1"

.                 global d2 = "pr2"

.                 global d3 = "pr3"

.                 label var $d1 "Dimension 1"

.                 label var $d2 "Dimension 2"

.                 label var $d3 "Dimension 3"

.                 sort cow year

.          
.                 merge cow year using vdem
(note: you are using old merge syntax; see [D] merge for new syntax)
(note: variable year was int, now double to accommodate using data's values)
(note: variable cowcode was float, now double to accommodate using data's values)

.                 tab gwf_country if _merge==1 /* VDem does not code UAE */

          Country name |      Freq.     Percent        Cum.
-----------------------+-----------------------------------
                   UAE |         39      100.00      100.00
-----------------------+-----------------------------------
                 Total |         39      100.00

.                 keep if gwf_caseid~=.
(6,574 observations deleted)

.                 drop _merge

.                 sort cow year

.                 merge cow year using polity
(note: you are using old merge syntax; see [D] merge for new syntax)
variables cowcode year do not uniquely identify observations in polity.dta

.                 tab _merge

     _merge |      Freq.     Percent        Cum.
------------+-----------------------------------
          1 |          3        0.03        0.03
          2 |      5,039       52.31       52.34
          3 |      4,591       47.66      100.00
------------+-----------------------------------
      Total |      9,633      100.00

.                 list cowcode gwf_casename year if _merge==1     /* Polity treats the
> se as pre-independence */,clean noobs

    cowcode    gwf_casename   year  
        616   Tunisia 56-NA   1957  
        616   Tunisia 56-NA   1958  
        690    Kuwait 61-NA   1962  

.                 drop _merge

.                 sort cow year

.                 merge cow year using uds
(note: you are using old merge syntax; see [D] merge for new syntax)
variables cowcode year do not uniquely identify observations in the master data

.                 tab _merge if gwf_caseid~=.

     _merge |      Freq.     Percent        Cum.
------------+-----------------------------------
          1 |          6        0.13        0.13
          3 |      4,588       99.87      100.00
------------+-----------------------------------
      Total |      4,594      100.00

.                 list gwf_casename year if _merge==1 & gwf_caseid~=., noobs clean    
>     

           gwf_casename   year  
    Germany, East 49-90   1990  
           Oman 1741-NA   1946  
           Oman 1741-NA   1947  
           Oman 1741-NA   1948  
           Oman 1741-NA   1949  
           Oman 1741-NA   1950  

.                 drop if gwf_caseid==.
(5,987 observations deleted)

.                 drop _merge

.                 sort cow year

.                 merge cow year using covariates
(note: you are using old merge syntax; see [D] merge for new syntax)
variables cowcode year do not uniquely identify observations in the master data
(note: variable country was str15, now str32 to accommodate using data's values)
(note: variable democracy was byte, now float to accommodate using data's values)

.                 drop if gwf_caseid==.
(3,362 observations deleted)

.                 tab _merge

     _merge |      Freq.     Percent        Cum.
------------+-----------------------------------
          3 |      4,594      100.00      100.00
------------+-----------------------------------
      Total |      4,594      100.00

.                 sort cow year

.                 gen repeat1 = year == year[_n-1] if cow==cow[_n-1]
(118 missing values generated)

.                 gen repeat2 = year == year[_n+1] if cow==cow[_n+1]
(118 missing values generated)

.                 drop if (repeat1==1 | repeat2==1) & polity2==.
(3 observations deleted)

.                 drop repeat* _merge n

.                 save temp,replace
(note: file temp.dta not found)
file temp.dta saved

.                 sort cow year

.                 rename se_irtpers8 seirtpers8

.                 outsheet cow year irtpers8 seirtpers8 using Personalism.csv ,replace
>  comma  /* save personalism data for R for sims of data in applied analysis */
(note: file Personalism.csv not found)

.                 spearman pr3 alphapers11 irtpers8 irtgrm
(obs=4591)

             |      pr3 alpha~11 irtpers8   irtgrm
-------------+------------------------------------
         pr3 |   1.0000 
 alphapers11 |   0.9100   1.0000 
    irtpers8 |   0.7946   0.9299   1.0000 
      irtgrm |   0.9009   0.9768   0.9257   1.0000 

.         
.                 * Plot dimensions by GWF regime types *
.                 local vars = "Military Personalist Party Monarchy"

.                 foreach z of local vars {
  2.                 twoway (scatter $d2 $d1 if `z'==0 & gwf_fail~=., title("`z'") msy
> mbol(circle) mfcolor(gs16) mcolor(gs13) saving(`z', replace) ) /*
>                 */ (scatter $d2 $d1 if `z'==1,  msymbol(circle) mcolor(gs3) mfcolor(
> gs16) scheme(lean1)  legend(off)   /*
>                 */ xline(-.5) yline(0) yscale(range (-2 2)) ylabel(-2 (1) 2,  glcolo
> r(gs14))  xscale(range (-2 2)) xlabel(-2 (1) 2,  glcolor(gs14)) )
  3.                 }
(note: scheme lean1 not found, using s2color)
(note: file Military.gph not found)
(file Military.gph saved)
(note: scheme lean1 not found, using s2color)
(note: file Personalist.gph not found)
(file Personalist.gph saved)
(note: scheme lean1 not found, using s2color)
(note: file Party.gph not found)
(file Party.gph saved)
(note: scheme lean1 not found, using s2color)
(note: file Monarchy.gph not found)
(file Monarchy.gph saved)

.                 gr combine Party.gph Military.gph Personalist.gph   Monarchy.gph  , 
> col(2)    ysize(5)

.                 *graph export "$dir/golden/LD12_GWF.pdf", as(pdf)  replace
.                 
.                 local vars = "Military Personalist Party Monarchy"

.                 foreach z of local vars {
  2.                 twoway (scatter $d3 $d2 if `z'==0 & gwf_fail~=., title("`z'") msy
> mbol(circle) mfcolor(gs16) mcolor(gs13) saving(`z', replace) ) /*
>                 */ (scatter $d3 $d2 if `z'==1,  msymbol(circle) mcolor(gs3) mfcolor(
> gs16) scheme(lean1)  legend(off)   /*
>                 */ xline(0.2) yline(0) yscale(range (-2 2)) ylabel(-2 (1) 2,  glcolo
> r(gs14))  xscale(range (-2 2)) xlabel(-2 (1) 2,  glcolor(gs14)) )
  3.                 }
(note: scheme lean1 not found, using s2color)
(file Military.gph saved)
(note: scheme lean1 not found, using s2color)
(file Personalist.gph saved)
(note: scheme lean1 not found, using s2color)
(file Party.gph saved)
(note: scheme lean1 not found, using s2color)
(file Monarchy.gph saved)

.                 gr combine Party.gph Military.gph Personalist.gph   Monarchy.gph  , 
> col(2)    ysize(5)

.                 *graph export "$dir/golden/LD23_GWF.pdf", as(pdf)  replace
.                 
.                 label var $d1 "Party"

.                 label var $d2 "Military"

.                 label var $d3 "Personal"

. **** This is Figure 4 code **** 
.                 * Plot dimensions for Party *
.                 scat3 $d1 $d2 $d3  if Party==1,variablenames spikes(lcol(gs14))mcol(
> blue)rot(30)elev(45)msym(o)msize(tiny) ///
>                         titlex("{&larr}low         Party           hi{&rarr}",mlaban
> g(18)msize(tiny)) ///
>                         titley("{&larr}low        Military          hi{&rarr}",mlaba
> ng(317)msize(tiny)) ///
>                         titlez("{&larr}low   Personalism     hi{&rarr}",msize(tiny))
>  ///
>                         title(Dominant party regimes,height(.1))saving(Party.gph,rep
> lace)       
note: projecting at rotate(30) elevate(45)
(file Party.gph saved)

.                 * Plot dimensions for Personalist *
.                 scat3 $d3 $d2 $d1  if Personalist==1,variablenames spikes(lcol(gs14)
> )mcol(blue)rot(30)elev(45)msym(o)msize(tiny) ///
>                         titlex("{&larr}low         Personalism           hi{&rarr}",
> mlabang(18)msize(tiny)) ///
>                         titley("{&larr}low        Military          hi{&rarr}",mlaba
> ng(317)msize(tiny)) ///
>                         titlez("{&larr}low      Party         hi{&rarr}", msize(tiny
> )) ///
>                         title(Personalist regimes,height(.1))saving(Personalist.gph,
> replace)    
note: projecting at rotate(30) elevate(45)
(file Personalist.gph saved)

.                 * Plot dimensions for Military *
.                 scat3  $d2 $d1 $d3  if Military==1,variablenames spikes(lcol(gs14))m
> col(blue)rot(30)elev(45)msym(o)msize(tiny) ///
>                         titlex("{&larr}low          Military          hi{&rarr}",mla
> bang(18)msize(tiny)) ///
>                         titley("{&larr}low        Party          hi{&rarr}",mlabang(
> 317)msize(tiny)) ///
>                         titlez("    {&larr}low   Personalism    hi{&rarr}",msize(tin
> y)) ///
>                         title(Military regimes,height(.1))saving(Military.gph,replac
> e)  
note: projecting at rotate(30) elevate(45)
(file Military.gph saved)

.                 * Plot dimensions for Monarchy *
.                 scat3 $d3 $d2 $d1  if Monarchy==1,variablenames spikes(lcol(gs14))mc
> ol(blue)rot(30)elev(45)msym(o)msize(tiny) ///
>                         titlex("{&larr}low         Personalism           hi{&rarr}",
> mlabang(18)msize(tiny)) ///
>                         titley("{&larr}low        Military          hi{&rarr}",mlaba
> ng(317)msize(tiny)) ///
>                         titlez("{&larr}low      Party         hi{&rarr}",msize(tiny)
> ) ///
>                         title(Monarchies,height(.1))saving(Monarchy.gph,replace)    
>     
note: projecting at rotate(30) elevate(45)
(file Monarchy.gph saved)

.                 gr combine Party.gph Military.gph Personalist.gph Monarchy.gph,col(2
> )iscale(.5)ysize(5)

.                 graph export "$dir/golden/LD_GWF.pdf", as(pdf)  replace
(file /Users/lee/Dropbox/Datavers/golden/LD_GWF.pdf written in PDF format)

.                 
.                 *** CORRELATIONS with GWF & WEEKS regimes ***
.                 corrtex $d1 $d2 $d3 Junta Strongman Boss Machine imil1 ipers1 Milita
> ry Monarchy Party Personalist , file(corrFact) replace      
(note: file corrFact.tex not found)


\begin{table}[htbp]\centering \caption{Cross-correlation table\label{corrtable}}
\begin{tabular}{l  c  c  c  c  c  c  c  c  c  c  c  c  c }\hline\hline
\multicolumn{1}{c}{Variables} &Party&Military&Personal&Junta&Strongman&Boss&Machine&im
> il1&ipers1&Military regime (including milpersonal and indirect military)&Monarchy&Pa
> rty regime (including hybrids, oligarchy, and Iran)&Personalist regime\\ \hline
Party&1.000\\
Military&-0.165&1.000\\
Personal&-0.103&0.077&1.000\\
Junta&-0.147&0.449&-0.121&1.000\\
Strongman&0.018&0.484&0.291&-0.146&1.000\\
Boss&0.242&-0.121&0.154&-0.153&-0.200&1.000\\
Machine&0.331&-0.304&-0.465&-0.158&-0.205&-0.216&1.000\\
imil1&-0.569&0.828&0.245&0.554&0.530&-0.286&-0.464&1.000\\
ipers1&0.052&0.073&0.650&-0.436&0.425&0.556&-0.596&0.043&1.000\\
Military regime (including milpersonal and indirect military)&-0.291&0.609&-0.167&0.52
> 4&0.054&-0.180&-0.179&0.516&-0.264&1.000\\
Monarchy&-0.675&-0.318&0.284&-0.124&-0.161&-0.172&-0.178&0.049&0.012&-0.147&1.000\\
Party regime (including hybrids, oligarchy, and Iran)&0.612&-0.336&-0.424&-0.131&-0.20
> 0&0.106&0.478&-0.570&-0.298&-0.359&-0.365&1.000\\
Personalist regime&0.045&0.198&0.413&-0.160&0.342&0.182&-0.256&0.204&0.557&-0.220&-0.2
> 23&-0.547&1.000\\
\hline \hline 
 \end{tabular}
\end{table}

 Output writted successfully in file : corrFact.tex

.                 pwcorr $d1 $d2 $d3 Junta Strongman Boss Machine imil1 ipers1 Militar
> y Monarchy Party Personalist  

             |      pr1      pr2      pr3    Junta Strong~n     Boss  Machine
-------------+---------------------------------------------------------------
         pr1 |   1.0000 
         pr2 |  -0.1649   1.0000 
         pr3 |  -0.1033   0.0773   1.0000 
       Junta |  -0.1469   0.4487  -0.1213   1.0000 
   Strongman |   0.0182   0.4839   0.2905  -0.1460   1.0000 
        Boss |   0.2420  -0.1206   0.1538  -0.1535  -0.1997   1.0000 
     Machine |   0.3311  -0.3038  -0.4651  -0.1576  -0.2051  -0.2156   1.0000 
       imil1 |  -0.5686   0.8281   0.2453   0.5540   0.5295  -0.2863  -0.4642 
      ipers1 |   0.0520   0.0732   0.6496  -0.4364   0.4253   0.5557  -0.5963 
    Military |  -0.2911   0.6088  -0.1669   0.5242   0.0543  -0.1799  -0.1791 
    Monarchy |  -0.6745  -0.3183   0.2844  -0.1243  -0.1609  -0.1715  -0.1782 
       Party |   0.6116  -0.3361  -0.4240  -0.1309  -0.2004   0.1056   0.4777 
 Personalist |   0.0453   0.1975   0.4132  -0.1599   0.3420   0.1817  -0.2560 

             |    imil1   ipers1 Military Monarchy    Party Person~t
-------------+------------------------------------------------------
       imil1 |   1.0000 
      ipers1 |   0.0431   1.0000 
    Military |   0.5165  -0.2640   1.0000 
    Monarchy |   0.0488   0.0116  -0.1466   1.0000 
       Party |  -0.5704  -0.2980  -0.3593  -0.3650   1.0000 
 Personalist |   0.2036   0.5566  -0.2199  -0.2233  -0.5475   1.0000 

. 
.                 *** WEEKS Strongman & Boss data ***
.                 twoway (hist $d2 if  Strongman==0 & Boss==1, bin(100) ylab(,glcol(gs
> 15)) title("Boss") saving(Boss, replace) xtitle("") xlab(-1 (1) 2) color(red)) 
(note: file Boss.gph not found)
(file Boss.gph saved)

.                 twoway (hist $d2 if  Strongman==1 & Boss==0, bin(100) ylab(,glcol(gs
> 15)) title("Strongman") saving(Strongman, replace) xlab(-1 (1) 2) color(blue))
(note: file Strongman.gph not found)
(file Strongman.gph saved)

.                 gr combine Boss.gph Strongman.gph  , col(1)    ysize(6) xcommon

.                 graph export "$dir/golden/LDBossStrongman.pdf", as(pdf)  replace
(file /Users/lee/Dropbox/Datavers/golden/LDBossStrongman.pdf written in PDF format)

.                 erase Boss.gph

.                 erase Strongman.gph

. 
.                 *** GWF Hybrid and Personalist regime data ***
.                 twoway (scatter $d3 $d1 if gwf_fail~=., xtitle(Party score) scheme(l
> ean1) ytitle(Personalism score) msymbol(circle) mfcolor(gs16) mcolor(gs13)) /*
>                 */ (scatter $d3 $d1 if gwf_regime=="party-personal"  , ylab(-2 (1) 2
> ,glcol(gs15)) xlab(-2 (1) 2) msymbol(x) mfcolor(gs16) mcolor(blue)  /*
>                 */ legend(label(1 "All observations") label(2  "Party-personal")  ri
> ng(1) pos(12) col(3)))
(note: scheme lean1 not found, using s2color)

.                 graph export "$dir/golden/PartyPersHybrid.pdf", as(pdf)  replace
(file /Users/lee/Dropbox/Datavers/golden/PartyPersHybrid.pdf written in PDF format)

.                 twoway (scatter $d3 $d2 if gwf_fail~=., xtitle(Military score) schem
> e(lean1) ytitle(Personalism score) msymbol(circle) mfcolor(gs16) mcolor(gs13)) /*
>                 */ (scatter $d3 $d2 if gwf_regime=="military-personal" , ylab(-2 (1)
>  2,glcol(gs15)) xlab(-2 (1) 2) msymbol(x) mfcolor(gs16) mcolor(blue)  /*
>                 */ legend(label(1 "All observations") label(2  "Military-personal") 
>   ring(1) pos(12) col(3)))
(note: scheme lean1 not found, using s2color)

.                         graph export "$dir/golden/MilPersHybrid.pdf", as(pdf)  repla
> ce
(file /Users/lee/Dropbox/Datavers/golden/MilPersHybrid.pdf written in PDF format)

.                 * Mengistu example *
.                 twoway (scatter $d3 $d2 if gwf_fail~=., xtitle(Military score) schem
> e(lean1) ytitle(Personalism score) msymbol(circle) mfcolor(gs16) mcolor(gs13)) /*
>                  */ (scatter $d3 $d2 if cow==530 & year>=1975 & year<=1991 & gwf_dur
> ation<=3, xlab(-2(1)2) ylab(-2(1)2,glcol(gs15)) msymbol(x) mfcolor(gs16) mcolor(blue
> ) /*
>                  */ legend(label(1 "All observations") label(2 "1975-77") label(3 "1
> 978-79") label(4 "1980-91") ring(1) pos(12) col(4)))  /*
>                  */ (scatter $d3 $d2 if cow==530 & year>=1975 & year<=1991 & (gwf_du
> ration==4 | gwf_duration==5),   msymbol(circle) mfcolor(gs16) mcolor(green) ) /*
>                  */ (scatter $d3 $d2 if cow==530 & year>=1975 & year<=1991 & gwf_dur
> ation>5,   msymbol(circle) mfcolor(gs16) mcolor(red) )
(note: scheme lean1 not found, using s2color)

.                 graph export "$dir/golden/MengistuHybrid.pdf", as(pdf)  replace
(file /Users/lee/Dropbox/Datavers/golden/MengistuHybrid.pdf written in PDF format)

.                 global bw = .5

.                 * Hybrid-personal regimes *
.                 twoway (kdensity $d3 if (gwf_regime=="party-personal" | gwf_regime==
> "military-personal" | gwf_regime=="party-personal-military") /*
>                 */ & gwf_duration<=1, bwidth($bw) color(red) lpattern(solid) title(H
> yrid personalist regimes, size(medium)) ytitle("Density") xtitle(Personalism score))
>  /*
>                 */ (kdensity $d3 if (gwf_regime=="party-personal" | gwf_regime=="mil
> itary-personal" | /*
>                 */ gwf_regime=="party-personal-military") & gwf_duration>1, bwidth($
> bw)  color(red) lpattern(dash) scheme(lean2) ylab(,glcolor(gs16)))  /*
>                 */ (kdensity $d3 if (gwf_regime=="party-personal" | /*
>                 */ gwf_regime=="military-personal" | gwf_regime=="party-personal-mil
> itary") & gwf_duration<=3,bwidth($bw) color(blue) lpattern(solid)) /*
>                 */ (kdensity $d3 if (gwf_regime=="party-personal" | /*
>                 */ gwf_regime=="military-personal" | gwf_regime=="party-personal-mil
> itary") & gwf_duration>3,bwidth($bw) color(blue) lpattern(dash)) /*
>                 */ (kdensity $d3 if (gwf_regime=="party-personal" | /*
>                 */ gwf_regime=="military-personal" | gwf_regime=="party-personal-mil
> itary") & gwf_duration<=6,bwidth($bw) color(green) lpattern(solid)) /*
>                 */ (kdensity $d3 if (gwf_regime=="party-personal" | /*
>                 */ gwf_regime=="military-personal" | gwf_regime=="party-personal-mil
> itary") & gwf_duration>6,bwidth($bw) color(green) lpattern(dash) /*
>                 */ legend(lab(1 "Year 1") lab(2 "Subsq. yrs") lab(3 "Years 1-3") lab
> (4 "Subsq. yrs")  /*
>                 */ lab(5 "Years 1-6") lab(6 "Subsq. yrs") symxsize(3) size(small) po
> s(11) col(1) ring(0)) xsize(7) ysize(4))
(note: scheme lean2 not found, using s2color)

.                 graph export "$dir/golden/PersHybrid.pdf", as(pdf)  replace
(file /Users/lee/Dropbox/Datavers/golden/PersHybrid.pdf written in PDF format)

.                 * Pure personal regimes *
.                 twoway (kdensity $d3 if (gwf_regime=="personal") /*
>                 */ & gwf_duration<=1, bwidth($bw) color(red) lpattern(solid)   title
> (Pure personalist regimes, size(medium)) ytitle("Density") xtitle(Personalism score)
> ) /*
>                 */ (kdensity $d3 if (gwf_regime=="personal") & gwf_duration>1, bwidt
> h($bw)  color(red) lpattern(dash) scheme(lean2) ylab(,glcolor(gs16)))  /*
>                 */ (kdensity $d3 if (gwf_regime=="personal") & gwf_duration<=3,bwidt
> h($bw) color(blue) lpattern(solid)) /*
>                 */ (kdensity $d3 if (gwf_regime=="personal") & gwf_duration>3,bwidth
> ($bw) color(blue) lpattern(dash)) /*
>                 */ (kdensity $d3 if (gwf_regime=="personal") & gwf_duration<=6,bwidt
> h($bw) color(green) lpattern(solid)) /*
>                 */ (kdensity $d3 if (gwf_regime=="personal") & gwf_duration>6,bwidth
> ($bw) color(green) lpattern(dash) /*
>                 */ legend(lab(1 "Year 1") lab(2 "Subsq. yrs") lab(3 "Years 1-3") lab
> (4 "Subsq. yrs")  /*
>                 */ lab(5 "Years 1-6") lab(6 "Subsq. yrs") symxsize(3) size(small) po
> s(11) col(1) ring(0)) xsize(7) ysize(4))
(note: scheme lean2 not found, using s2color)

.                 graph export "$dir/golden/PersPure.pdf", as(pdf)  replace
(file /Users/lee/Dropbox/Datavers/golden/PersPure.pdf written in PDF format)

.                 * Formal test of duration time and personalism *
.                 gen p = gwf_regime=="personal"

.                 gen phybrid = gwf_regime=="party-personal"|gwf_regime=="military-per
> sonal"| gwf_regime=="party-personal-military"

.                 gen pXld = p*ld

.                 gen ldXphybrid = phybrid*ld

.                 gen decade = year<1960

.                 replace decade = 2 if year>=1960 & year<1970
(744 real changes made)

.                 replace decade = 3 if year>=1970 & year<1980
(937 real changes made)

.                 replace decade = 4 if year>=1980 & year<1990
(911 real changes made)

.                 replace decade = 5 if year>=1990 & year<2000
(721 real changes made)

.                 xi:ivreg2 $d3 i.case ld pXld ldXphybrid,cluster(case)partial(i.case)
>  /* regime FE account for regime type dummies */
i.casename        _Icasename_1-280    (_Icasename_1 for cas~e==Afghanistan 09-NA omitt
> ed)

OLS estimation
--------------

Estimates efficient for homoskedasticity only
Statistics robust to heteroskedasticity and clustering on casename

Number of clusters (casename) =    280                Number of obs =     4591
                                                      F(  3,   279) =    16.80
                                                      Prob > F      =   0.0000
Total (centered) SS     =   756.441152                Centered R2   =   0.1487
Total (uncentered) SS   =   756.441152                Uncentered R2 =   0.1487
Residual SS             =  643.9816071                Root MSE      =    .3745

------------------------------------------------------------------------------
             |               Robust
         pr3 |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
          ld |  -.0360481    .037224    -0.97   0.333    -.1090058    .0369096
        pXld |   .4184444   .0682775     6.13   0.000     .2846229    .5522658
  ldXphybrid |   .2371381   .0790069     3.00   0.003     .0822874    .3919888
------------------------------------------------------------------------------
Included instruments: ld pXld ldXphybrid
Partialled-out:       _Icasename_2 _Icasename_3 _Icasename_4 _Icasename_5
                      _Icasename_6 _Icasename_7 _Icasename_8 _Icasename_9
                      _Icasename_10 _Icasename_11 _Icasename_12 _Icasename_13
                      _Icasename_14 _Icasename_15 _Icasename_16 _Icasename_17
                      _Icasename_18 _Icasename_19 _Icasename_20 _Icasename_21
                      _Icasename_22 _Icasename_23 _Icasename_24 _Icasename_25
                      _Icasename_26 _Icasename_27 _Icasename_28 _Icasename_29
                      _Icasename_30 _Icasename_31 _Icasename_32 _Icasename_33
                      _Icasename_34 _Icasename_35 _Icasename_36 _Icasename_37
                      _Icasename_38 _Icasename_39 _Icasename_40 _Icasename_41
                      _Icasename_42 _Icasename_43 _Icasename_44 _Icasename_45
                      _Icasename_46 _Icasename_47 _Icasename_48 _Icasename_49
                      _Icasename_50 _Icasename_51 _Icasename_52 _Icasename_53
                      _Icasename_54 _Icasename_55 _Icasename_56 _Icasename_57
                      _Icasename_58 _Icasename_59 _Icasename_60 _Icasename_61
                      _Icasename_62 _Icasename_63 _Icasename_64 _Icasename_65
                      _Icasename_66 _Icasename_67 _Icasename_68 _Icasename_69
                      _Icasename_70 _Icasename_71 _Icasename_72 _Icasename_73
                      _Icasename_74 _Icasename_75 _Icasename_76 _Icasename_77
                      _Icasename_78 _Icasename_79 _Icasename_80 _Icasename_81
                      _Icasename_82 _Icasename_83 _Icasename_84 _Icasename_85
                      _Icasename_86 _Icasename_87 _Icasename_88 _Icasename_89
                      _Icasename_90 _Icasename_91 _Icasename_92 _Icasename_93
                      _Icasename_94 _Icasename_95 _Icasename_96 _Icasename_97
                      _Icasename_98 _Icasename_99 _Icasename_100 _Icasename_101
                      _Icasename_102 _Icasename_103 _Icasename_104
                      _Icasename_105 _Icasename_106 _Icasename_107
                      _Icasename_108 _Icasename_109 _Icasename_110
                      _Icasename_111 _Icasename_112 _Icasename_113
                      _Icasename_114 _Icasename_115 _Icasename_116
                      _Icasename_117 _Icasename_118 _Icasename_119
                      _Icasename_120 _Icasename_121 _Icasename_122
                      _Icasename_123 _Icasename_124 _Icasename_125
                      _Icasename_126 _Icasename_127 _Icasename_128
                      _Icasename_129 _Icasename_130 _Icasename_131
                      _Icasename_132 _Icasename_133 _Icasename_134
                      _Icasename_135 _Icasename_136 _Icasename_137
                      _Icasename_138 _Icasename_139 _Icasename_140
                      _Icasename_141 _Icasename_142 _Icasename_143
                      _Icasename_144 _Icasename_145 _Icasename_146
                      _Icasename_147 _Icasename_148 _Icasename_149
                      _Icasename_150 _Icasename_151 _Icasename_152
                      _Icasename_153 _Icasename_154 _Icasename_155
                      _Icasename_156 _Icasename_157 _Icasename_158
                      _Icasename_159 _Icasename_160 _Icasename_161
                      _Icasename_162 _Icasename_163 _Icasename_164
                      _Icasename_165 _Icasename_166 _Icasename_167
                      _Icasename_168 _Icasename_169 _Icasename_170
                      _Icasename_171 _Icasename_172 _Icasename_173
                      _Icasename_174 _Icasename_175 _Icasename_176
                      _Icasename_177 _Icasename_178 _Icasename_179
                      _Icasename_180 _Icasename_181 _Icasename_182
                      _Icasename_183 _Icasename_184 _Icasename_185
                      _Icasename_186 _Icasename_187 _Icasename_188
                      _Icasename_189 _Icasename_190 _Icasename_191
                      _Icasename_192 _Icasename_193 _Icasename_194
                      _Icasename_195 _Icasename_196 _Icasename_197
                      _Icasename_198 _Icasename_199 _Icasename_200
                      _Icasename_201 _Icasename_202 _Icasename_203
                      _Icasename_204 _Icasename_205 _Icasename_206
                      _Icasename_207 _Icasename_208 _Icasename_209
                      _Icasename_210 _Icasename_211 _Icasename_212
                      _Icasename_213 _Icasename_214 _Icasename_215
                      _Icasename_216 _Icasename_217 _Icasename_218
                      _Icasename_219 _Icasename_220 _Icasename_221
                      _Icasename_222 _Icasename_223 _Icasename_224
                      _Icasename_225 _Icasename_226 _Icasename_227
                      _Icasename_228 _Icasename_229 _Icasename_230
                      _Icasename_231 _Icasename_232 _Icasename_233
                      _Icasename_234 _Icasename_235 _Icasename_236
                      _Icasename_237 _Icasename_238 _Icasename_239
                      _Icasename_240 _Icasename_241 _Icasename_242
                      _Icasename_243 _Icasename_244 _Icasename_245
                      _Icasename_246 _Icasename_247 _Icasename_248
                      _Icasename_249 _Icasename_250 _Icasename_251
                      _Icasename_252 _Icasename_253 _Icasename_254
                      _Icasename_255 _Icasename_256 _Icasename_257
                      _Icasename_258 _Icasename_259 _Icasename_260
                      _Icasename_261 _Icasename_262 _Icasename_263
                      _Icasename_264 _Icasename_265 _Icasename_266
                      _Icasename_267 _Icasename_268 _Icasename_269
                      _Icasename_270 _Icasename_271 _Icasename_272
                      _Icasename_273 _Icasename_274 _Icasename_275
                      _Icasename_276 _Icasename_277 _Icasename_278
                      _Icasename_279 _Icasename_280 _cons
                      nb: total SS, model F and R2s are after partialling-out;
                          any small-sample adjustments include partialled-out
                          variables in regressor count K
------------------------------------------------------------------------------

.                 est store p1

.                 lincom ld

 ( 1)  ld = 0

------------------------------------------------------------------------------
         pr3 |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         (1) |  -.0360481    .037224    -0.97   0.333    -.1090058    .0369096
------------------------------------------------------------------------------

.                 lincom ldXphybrid

 ( 1)  ldXphybrid = 0

------------------------------------------------------------------------------
         pr3 |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         (1) |   .2371381   .0790069     3.00   0.003     .0822874    .3919888
------------------------------------------------------------------------------

.                 lincom ldXphybrid + ld

 ( 1)  ld + ldXphybrid = 0

------------------------------------------------------------------------------
         pr3 |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         (1) |     .20109   .0696884     2.89   0.004     .0645033    .3376767
------------------------------------------------------------------------------

.                 lincom pXld

 ( 1)  pXld = 0

------------------------------------------------------------------------------
         pr3 |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         (1) |   .4184444   .0682775     6.13   0.000     .2846229    .5522658
------------------------------------------------------------------------------

.                 lincom pXld + ld

 ( 1)  ld + pXld = 0

------------------------------------------------------------------------------
         pr3 |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         (1) |   .3823963    .057238     6.68   0.000     .2702118    .4945807
------------------------------------------------------------------------------

.                 label var ld "Regime duration (log)"

.                 label var ldX "Duration X hybrid"

.                 label var pXld "Duration X personalist"

.                 label var ld "Regime duration (log)"

.                 coefplot p1, drop(_I* _cons) level(95) grid(glcolor(gs16)) order(ld 
> ldX pX) text(0.7 1.35 "95% ci", place(c)) xtitle(Coefficient estimate) xline(0, lpat
> tern(dash))

.                 graph export "$dir/golden/PersDuration.pdf", as(pdf)  replace
(file /Users/lee/Dropbox/Datavers/golden/PersDuration.pdf written in PDF format)

.         
.                   ** China over time ***
.                   twoway (line $d1 $d2 $d3 year if cow==710 & year>1948 & year<2011,
>  lcolor(blue green red) yscale(range(-2 1)) ylab(-2 (1) 1,glcol(gs15)) ///
>                                 xscale(range (1950 2010)) xlabel(1950 (10) 2010) tit
> le("Dimensions of dictatorship, China 1949-2010") ///
>                                 ytitle("Latent dimensions") xtitle("Year",height(6))
>   legend(lab(1 "party strength") lab(2 "military autonomy")  ///
>                                 lab(3 "personalism") pos(6) col(3) ring(2)))  

.                   graph export "$dir/golden/China.pdf", as(pdf) replace
(file /Users/lee/Dropbox/Datavers/golden/China.pdf written in PDF format)

.   
.  
.                 
.         ********************************************
.         ** Correlations with CGV, Svolik, and DPI **
.         ********************************************
.                 use temp,clear

.                 sort cow  year

.                 merge cow year using ATH
(note: you are using old merge syntax; see [D] merge for new syntax)
(note: variable country was str32, now str33 to accommodate using data's values)

.                 tab _merge

     _merge |      Freq.     Percent        Cum.
------------+-----------------------------------
          2 |      1,084       19.10       19.10
          3 |      4,591       80.90      100.00
------------+-----------------------------------
      Total |      5,675      100.00

.                 gen cg_mil = cg_regime==4 if cg_regime~=.
(453 missing values generated)

.                 gen cg_party =  cg_gparties>=1 if cg_gparties~=.
(287 missing values generated)

.                 gen dpi_party = dpi_parties >=1 if dpi_parties~=.
(2,573 missing values generated)

. 
.                 *Label variables**
.                 label var cg_gparties "Number of parties (Gandhi)"

.                 label var ht_parties "Number of parties (H&T)"

.                 label var dpi_parties "Number of parties (DPI)"

.                 label var sv_parties "Number of parties (Svolik)"

.                 label var cg_party "One or more parties (Gandhi)"

.                 label var ht_party "One or more parties (H&T)"

.                 label var dpi_party "One or more parties (DPI)"

.                 label var sv_party "One of more parties (Svolik)"

.                 label var lparty "Leader associated w. party (Svolik)"

.                 label var cg_ginst "Institutions (Gandhi)"

.                 label var sv_legindex "Legislative competitive index (Svolik)"

.                 label var dpi_liec "Legislative competitive index (DPI)"

.                 label var cg_mil  "Military regime (Gandhi)"

.                 label var ht_mil  "Military regime (H&T)"

.                 label var dpi_mil  "Military regime (DPI)"

.                 label var sv_military  "Military regime (Svolik)"

.                 label var sv_mil_corp "Corporate military (Svolik)"

.                 label var sv_mil_per "Personal military (Svolik)"

.                 label var sv_mil_ind "Indirect military (Svolik)"       

.                 global var_party ="cg_gparties ht_parties dpi_parties sv_parties cg_
> party ht_party dpi_party sv_party lparty cg_ginst sv_legindex dpi_liec"

.                 global var_mil = "cg_mil ht_mil dpi_mil sv_military sv_mil_corp sv_m
> il_pers sv_mil_ind"

.                 
.                 * CG comparison *
.                 factor $allvar1 $allvar2 $allvar3 $allvar4 $allvar5 $allvar6 $allvar
> 7 $allvar8 $allvar9 $allvar10 if cg_gparties~=., factors(4)
(obs=4,386)
(collinear variables specified)

Factor analysis/correlation                      Number of obs    =      4,386
    Method: principal factors                    Retained factors =          4
    Rotation: (unrotated)                        Number of params =        330

    --------------------------------------------------------------------------
         Factor  |   Eigenvalue   Difference        Proportion   Cumulative
    -------------+------------------------------------------------------------
        Factor1  |     10.91990      3.72789            0.1606       0.1606
        Factor2  |      7.19201      1.71655            0.1058       0.2664
        Factor3  |      5.47546      0.55615            0.0805       0.3470
        Factor4  |      4.91931      1.81946            0.0724       0.4193
        Factor5  |      3.09985      0.42184            0.0456       0.4649
        Factor6  |      2.67801      0.19312            0.0394       0.5043
        Factor7  |      2.48489      0.34704            0.0366       0.5409
        Factor8  |      2.13785      0.20612            0.0314       0.5723
        Factor9  |      1.93173      0.06297            0.0284       0.6007
       Factor10  |      1.86876      0.20184            0.0275       0.6282
       Factor11  |      1.66692      0.16206            0.0245       0.6528
       Factor12  |      1.50485      0.02546            0.0221       0.6749
       Factor13  |      1.47940      0.06614            0.0218       0.6967
       Factor14  |      1.41326      0.07565            0.0208       0.7174
       Factor15  |      1.33761      0.05077            0.0197       0.7371
       Factor16  |      1.28684      0.11476            0.0189       0.7561
       Factor17  |      1.17209      0.03103            0.0172       0.7733
       Factor18  |      1.14106      0.03622            0.0168       0.7901
       Factor19  |      1.10484      0.04824            0.0163       0.8063
       Factor20  |      1.05660      0.08228            0.0155       0.8219
       Factor21  |      0.97432      0.05391            0.0143       0.8362
       Factor22  |      0.92041      0.02970            0.0135       0.8497
       Factor23  |      0.89071      0.03119            0.0131       0.8629
       Factor24  |      0.85952      0.03552            0.0126       0.8755
       Factor25  |      0.82401      0.04353            0.0121       0.8876
       Factor26  |      0.78048      0.06279            0.0115       0.8991
       Factor27  |      0.71769      0.02280            0.0106       0.9097
       Factor28  |      0.69489      0.04156            0.0102       0.9199
       Factor29  |      0.65333      0.02676            0.0096       0.9295
       Factor30  |      0.62657      0.05143            0.0092       0.9387
       Factor31  |      0.57513      0.06048            0.0085       0.9472
       Factor32  |      0.51465      0.02749            0.0076       0.9547
       Factor33  |      0.48716      0.04414            0.0072       0.9619
       Factor34  |      0.44302      0.02588            0.0065       0.9684
       Factor35  |      0.41714      0.04555            0.0061       0.9746
       Factor36  |      0.37159      0.02371            0.0055       0.9800
       Factor37  |      0.34789      0.00848            0.0051       0.9851
       Factor38  |      0.33941      0.01605            0.0050       0.9901
       Factor39  |      0.32336      0.02837            0.0048       0.9949
       Factor40  |      0.29499      0.04890            0.0043       0.9992
       Factor41  |      0.24609      0.00604            0.0036       1.0028
       Factor42  |      0.24006      0.03635            0.0035       1.0064
       Factor43  |      0.20370      0.02054            0.0030       1.0094
       Factor44  |      0.18317      0.02369            0.0027       1.0121
       Factor45  |      0.15948      0.01451            0.0023       1.0144
       Factor46  |      0.14497      0.01654            0.0021       1.0165
       Factor47  |      0.12844      0.01818            0.0019       1.0184
       Factor48  |      0.11026      0.00878            0.0016       1.0201
       Factor49  |      0.10147      0.01292            0.0015       1.0216
       Factor50  |      0.08856      0.02374            0.0013       1.0229
       Factor51  |      0.06482      0.00412            0.0010       1.0238
       Factor52  |      0.06070      0.02015            0.0009       1.0247
       Factor53  |      0.04055      0.00844            0.0006       1.0253
       Factor54  |      0.03212      0.00991            0.0005       1.0258
       Factor55  |      0.02220      0.00738            0.0003       1.0261
       Factor56  |      0.01483      0.00671            0.0002       1.0263
       Factor57  |      0.00811      0.00595            0.0001       1.0264
       Factor58  |      0.00217      0.00082            0.0000       1.0265
       Factor59  |      0.00135      0.00135            0.0000       1.0265
       Factor60  |      0.00000      0.00000            0.0000       1.0265
       Factor61  |      0.00000      0.00000            0.0000       1.0265
       Factor62  |      0.00000      0.00000            0.0000       1.0265
       Factor63  |     -0.00000      0.00000           -0.0000       1.0265
       Factor64  |     -0.00000      0.00747           -0.0000       1.0265
       Factor65  |     -0.00747      0.00710           -0.0001       1.0264
       Factor66  |     -0.01457      0.00052           -0.0002       1.0262
       Factor67  |     -0.01509      0.00941           -0.0002       1.0259
       Factor68  |     -0.02449      0.00722           -0.0004       1.0256
       Factor69  |     -0.03171      0.01528           -0.0005       1.0251
       Factor70  |     -0.04699      0.00854           -0.0007       1.0244
       Factor71  |     -0.05553      0.01412           -0.0008       1.0236
       Factor72  |     -0.06964      0.00753           -0.0010       1.0226
       Factor73  |     -0.07717      0.00931           -0.0011       1.0214
       Factor74  |     -0.08649      0.00651           -0.0013       1.0202
       Factor75  |     -0.09299      0.00853           -0.0014       1.0188
       Factor76  |     -0.10153      0.01007           -0.0015       1.0173
       Factor77  |     -0.11160      0.00491           -0.0016       1.0157
       Factor78  |     -0.11651      0.00658           -0.0017       1.0140
       Factor79  |     -0.12309      0.01976           -0.0018       1.0121
       Factor80  |     -0.14285      0.00440           -0.0021       1.0100
       Factor81  |     -0.14724      0.01000           -0.0022       1.0079
       Factor82  |     -0.15725      0.01230           -0.0023       1.0056
       Factor83  |     -0.16955      0.03912           -0.0025       1.0031
       Factor84  |     -0.20867            .           -0.0031       1.0000
    --------------------------------------------------------------------------
    LR test: independent vs. saturated: chi2(3486)=       . Prob>chi2 =      .

Factor loadings (pattern matrix) and unique variances

    ---------------------------------------------------------------------
        Variable |  Factor1   Factor2   Factor3   Factor4 |   Uniqueness 
    -------------+----------------------------------------+--------------
    partyrbrstmp |   0.1502    0.3565    0.4501    0.1666 |      0.6200  
       militrank |  -0.5910    0.6299   -0.1203    0.1317 |      0.2221  
     ldrrotation |  -0.1955    0.1930   -0.2328    0.0881 |      0.8626  
      milconsult |  -0.4694    0.3452   -0.3557    0.0530 |      0.5311  
    milmerit_mil |   0.1343   -0.0289   -0.5748    0.1570 |      0.6261  
    milmeritpers |  -0.1435    0.1274    0.4944   -0.1743 |      0.6884  
      milnotrial |  -0.1155    0.3071    0.4374   -0.1966 |      0.6624  
      plebiscite |   0.0313    0.2358    0.2324    0.2959 |      0.8018  
        heirclan |  -0.1442   -0.2721    0.3015   -0.2525 |      0.7506  
      officepers |  -0.1160   -0.0524    0.6324    0.0791 |      0.5776  
     paramilpers |  -0.2028   -0.0926    0.5079   -0.0061 |      0.6923  
    ParamilParty |   0.3815    0.1251   -0.1483    0.0667 |      0.8123  
     ParamilFReb |  -0.0950    0.1297   -0.1913   -0.1453 |      0.9164  
    supportparty |   0.7529    0.5245    0.2966    0.0228 |      0.0697  
     partyleader |   0.6133    0.3195    0.2386   -0.0469 |      0.4627  
     localorgzns |   0.7554    0.4780    0.2265    0.0161 |      0.1492  
       partymins |   0.7274    0.3982    0.1369   -0.0375 |      0.2921  
       excomcivn |   0.7348    0.3213    0.0779   -0.0042 |      0.3507  
     multiethnic |   0.5772    0.2856   -0.0177    0.1225 |      0.5700  
      monoethnic |   0.1035    0.2105    0.3318   -0.1194 |      0.8206  
       heirparty |   0.7680    0.1096   -0.2484    0.0025 |      0.3364  
      heirfamily |  -0.3927   -0.3175    0.4956   -0.1131 |      0.4866  
      legcompetn |   0.3562   -0.0548    0.1434    0.3444 |      0.7309  
    leaderrela~s |  -0.1592   -0.2616    0.4607   -0.1170 |      0.6803  
       leaderciv |   0.4905   -0.6810   -0.0625    0.3606 |      0.1617  
       leadermil |  -0.6441    0.6766   -0.0340    0.1390 |      0.1068  
     leaderrebel |   0.2046    0.0530    0.1537   -0.7981 |      0.2947  
         heirciv |   0.6633   -0.2705   -0.2365    0.0703 |      0.4260  
          cabciv |   0.3832   -0.4350    0.1303    0.0208 |      0.6465  
          cabmil |  -0.3808    0.4978   -0.1470   -0.0267 |      0.5848  
      partymilit |   0.5965    0.1278   -0.2316   -0.2967 |      0.4862  
       ldrPriorD |   0.2647   -0.1519    0.1523    0.4438 |      0.6867  
        ldrParty |   0.5206    0.0023   -0.3318    0.0686 |      0.6142  
          ldrMil |  -0.6306    0.5768   -0.0966    0.1458 |      0.2391  
        ldrRebel |   0.1698    0.0522    0.1486   -0.7391 |      0.4002  
          ldrCiv |   0.0832   -0.0133    0.0278    0.1180 |      0.9782  
        ldrOther |  -0.0501   -0.0088    0.1980    0.0441 |      0.9563  
        ldrForgn |   0.1407   -0.0373    0.0419   -0.0097 |      0.9770  
        ldrHered |  -0.3310   -0.7136    0.1103   -0.0799 |      0.3626  
        SeizCoup |  -0.5617    0.5265   -0.0033    0.1659 |      0.3797  
       SeizRebel |   0.2507   -0.0662   -0.0623   -0.6992 |      0.4400  
       SeizUpris |  -0.0146   -0.0096   -0.0937    0.0587 |      0.9875  
        SeizElec |   0.3549   -0.1500    0.0738    0.4801 |      0.6156  
        SeizSucc |  -0.0268    0.0609    0.0797    0.0660 |      0.9849  
         SeizFam |  -0.2303   -0.5032    0.0761   -0.0238 |      0.6874  
     PartyhNoWin |   0.1431    0.1383    0.0649   -0.0886 |      0.9483  
       PartyhWin |   0.0474    0.0129    0.0489    0.0602 |      0.9916  
       PartyhReb |   0.3505    0.1038   -0.1669   -0.5901 |      0.4903  
    PartyhPrio~m |   0.4467   -0.0124    0.0461    0.4290 |      0.6141  
    PartyhNopa~y |  -0.7529   -0.5245   -0.2966   -0.0228 |      0.0697  
      PartyhElec |   0.0073   -0.0193    0.1290    0.0777 |      0.9769  
    MilPartyAlly |  -0.0793    0.2244    0.0407    0.0746 |      0.9361  
      MilPartyNo |  -0.6128    0.1379   -0.4504    0.0378 |      0.4012  
    MilPartyPr~r |  -0.0551    0.3720    0.0374    0.0437 |      0.8553  
      nomilitary |   0.0826   -0.1884    0.0446    0.1538 |      0.9320  
    milethnic_~e |   0.1381    0.2003   -0.2868    0.0331 |      0.8575  
    milethnic~ro |  -0.0648   -0.2284    0.1206   -0.0478 |      0.9268  
    milethnic~mo |  -0.1465    0.1204    0.2110   -0.0625 |      0.9156  
    sectyapp_p~y |   0.4311    0.0534   -0.4184   -0.1788 |      0.6043  
    sectyapppers |  -0.1300   -0.1294    0.6244   -0.0077 |      0.5765  
    ElecldrPrD~t |   0.0295   -0.0421    0.0474    0.0524 |      0.9924  
    ElecldrPrDem |   0.0770   -0.1196    0.0425    0.1936 |      0.9405  
      ElecldrNot |  -0.1571    0.2869   -0.4036   -0.3218 |      0.6266  
       Elecldr1C |   0.1255    0.2436    0.2644    0.1716 |      0.8255  
       Elecldr1F |   0.1124    0.0278    0.0594   -0.0635 |      0.9790  
     ElecldrMLeg |   0.1988   -0.0545   -0.0480    0.1175 |      0.9414  
    ElecldrMExec |   0.1370    0.0736    0.1001    0.1607 |      0.9399  
    legnoms_in~t |   0.0029    0.0169   -0.0791   -0.3446 |      0.8747  
    legnoms_veto |   0.1770    0.1062    0.2293    0.2472 |      0.8437  
    legnoms_no~o |   0.0779   -0.2545   -0.1167    0.1408 |      0.8957  
    legnoms_pr~m |   0.0632   -0.0773    0.0193    0.1677 |      0.9615  
      LdrexHighR |  -0.5239    0.5558   -0.1615    0.1286 |      0.3739  
       LdrexLowR |  -0.2702    0.2699    0.1565    0.0562 |      0.8265  
      LdrexRebel |   0.1939    0.0308    0.1629   -0.8058 |      0.2856  
      LdrexDemEl |   0.3477   -0.1611    0.1299    0.4552 |      0.6291  
      LdrexParty |   0.5489   -0.0330   -0.3365    0.0570 |      0.5811  
      LdrexLoyal |   0.0477    0.0159   -0.0002    0.0751 |      0.9918  
      LdrexReltv |  -0.0003    0.0122    0.1645    0.0159 |      0.9725  
     LdrexRulFam |  -0.3392   -0.7399    0.1204   -0.0754 |      0.3173  
      LdrexOther |  -0.0442   -0.1237    0.0242    0.0894 |      0.9742  
    partye~mpers |   0.1630    0.3659    0.5153    0.0446 |      0.5721  
    partyexcom~n |   0.3880    0.0910   -0.2132   -0.1001 |      0.7857  
    partyexcom~e |   0.3022    0.0121   -0.2002    0.0214 |      0.8680  
     createparty |  -0.1672    0.3582    0.4141    0.0518 |      0.6696  
    ---------------------------------------------------------------------

.                 rotate, promax(3) 

Factor analysis/correlation                      Number of obs    =      4,386
    Method: principal factors                    Retained factors =          4
    Rotation: oblique promax (Kaiser off)        Number of params =        330

    --------------------------------------------------------------------------
         Factor  |     Variance   Proportion    Rotated factors are correlated
    -------------+------------------------------------------------------------
        Factor1  |      9.40671       0.1384
        Factor2  |      8.89680       0.1309
        Factor3  |      6.15761       0.0906
        Factor4  |      4.93706       0.0726
    --------------------------------------------------------------------------
    LR test: independent vs. saturated: chi2(3486)=       . Prob>chi2 =      .

Rotated factor loadings (pattern matrix) and unique variances

    ---------------------------------------------------------------------
        Variable |  Factor1   Factor2   Factor3   Factor4 |   Uniqueness 
    -------------+----------------------------------------+--------------
    partyrbrstmp |   0.4977    0.1642    0.3735   -0.1075 |      0.6200  
       militrank |   0.0039    0.8836   -0.0242   -0.1475 |      0.2221  
     ldrrotation |  -0.0667    0.3103   -0.1971   -0.1055 |      0.8626  
      milconsult |  -0.1877    0.6050   -0.2610   -0.0899 |      0.5311  
    milmerit_mil |  -0.0981   -0.0047   -0.5972   -0.1816 |      0.6261  
    milmeritpers |   0.1340    0.1029    0.5146    0.1993 |      0.6884  
      milnotrial |   0.2584    0.2449    0.4407    0.2311 |      0.6624  
      plebiscite |   0.2731    0.1693    0.1930   -0.2660 |      0.8018  
        heirclan |  -0.2069   -0.2106    0.3565    0.2425 |      0.7506  
      officepers |   0.0855   -0.0647    0.6465   -0.0547 |      0.5776  
     paramilpers |  -0.0448   -0.0388    0.5513    0.0141 |      0.6923  
    ParamilParty |   0.3037   -0.0799   -0.2471   -0.0379 |      0.8123  
     ParamilFReb |  -0.0424    0.1784   -0.1681    0.1345 |      0.9164  
    supportparty |   0.9679   -0.0168    0.0728    0.0846 |      0.0697  
     partyleader |   0.7093   -0.1086    0.0661    0.1278 |      0.4627  
     localorgzns |   0.9155   -0.0477    0.0072    0.0846 |      0.1492  
       partymins |   0.8104   -0.0904   -0.0663    0.1261 |      0.2921  
       excomcivn |   0.7458   -0.1482   -0.1216    0.0854 |      0.3507  
     multiethnic |   0.5914   -0.0679   -0.1793   -0.0613 |      0.5700  
      monoethnic |   0.3121    0.0633    0.2891    0.1593 |      0.8206  
       heirparty |   0.5211   -0.2973   -0.4340    0.0498 |      0.3364  
      heirfamily |  -0.3393   -0.1288    0.6040    0.0922 |      0.4866  
      legcompetn |   0.2681   -0.2385    0.0468   -0.3115 |      0.7309  
    leaderrela~s |  -0.1529   -0.2064    0.5103    0.1157 |      0.6803  
       leaderciv |  -0.1366   -0.8086   -0.1438   -0.3662 |      0.1617  
       leadermil |   0.0273    0.9404    0.0693   -0.1512 |      0.1068  
     leaderrebel |   0.1786   -0.1493    0.1252    0.8263 |      0.2947  
         heirciv |   0.1946   -0.5555   -0.3734   -0.0481 |      0.4260  
          cabciv |   0.0013   -0.5949    0.0650   -0.0091 |      0.6465  
          cabmil |   0.0383    0.6484   -0.0861    0.0180 |      0.5848  
      partymilit |   0.4055   -0.2113   -0.3675    0.3376 |      0.4862  
       ldrPriorD |   0.1471   -0.2636    0.0807   -0.4233 |      0.6867  
        ldrParty |   0.2566   -0.2337   -0.4512   -0.0469 |      0.6142  
          ldrMil |  -0.0516    0.8585    0.0115   -0.1666 |      0.2391  
        ldrRebel |   0.1561   -0.1256    0.1266    0.7641 |      0.4002  
          ldrCiv |   0.0626   -0.0526    0.0041   -0.1106 |      0.9782  
        ldrOther |   0.0237   -0.0048    0.2045   -0.0371 |      0.9563  
        ldrForgn |   0.0823   -0.1160    0.0102    0.0210 |      0.9770  
        ldrHered |  -0.6890   -0.4383    0.2390    0.0180 |      0.3626  
        SeizCoup |  -0.0095    0.7662    0.0890   -0.1788 |      0.3797  
       SeizRebel |   0.0660   -0.2368   -0.0920    0.7113 |      0.4400  
       SeizUpris |  -0.0423    0.0177   -0.0894   -0.0658 |      0.9875  
        SeizElec |   0.1874   -0.2979   -0.0190   -0.4569 |      0.6156  
        SeizSucc |   0.0523    0.0594    0.0778   -0.0598 |      0.9849  
         SeizFam |  -0.4823   -0.3082    0.1650   -0.0197 |      0.6874  
     PartyhNoWin |   0.2081    0.0213    0.0227    0.1118 |      0.9483  
       PartyhWin |   0.0597   -0.0180    0.0334   -0.0528 |      0.9916  
       PartyhReb |   0.2252   -0.1262   -0.2336    0.6141 |      0.4903  
    PartyhPrio~m |   0.3336   -0.2329   -0.0758   -0.3921 |      0.6141  
    PartyhNopa~y |  -0.9679    0.0168   -0.0728   -0.0846 |      0.0697  
      PartyhElec |   0.0360   -0.0331    0.1229   -0.0708 |      0.9769  
    MilPartyAlly |   0.1181    0.2322    0.0407   -0.0651 |      0.9361  
      MilPartyNo |  -0.4588    0.5225   -0.3044   -0.1037 |      0.4012  
    MilPartyPr~r |   0.2339    0.3411    0.0225   -0.0237 |      0.8553  
      nomilitary |  -0.0517   -0.1992    0.0315   -0.1559 |      0.9320  
    milethnic_~e |   0.1453    0.1355   -0.3282   -0.0270 |      0.8575  
    milethnic~ro |  -0.1674   -0.1769    0.1507    0.0361 |      0.9268  
    milethnic~mo |   0.0456    0.1475    0.2350    0.0704 |      0.9156  
    sectyapp_p~y |   0.1903   -0.1469   -0.5094    0.1915 |      0.6043  
    sectyapppers |   0.0153   -0.1269    0.6503    0.0261 |      0.5765  
    ElecldrPrD~t |   0.0086   -0.0547    0.0403   -0.0498 |      0.9924  
    ElecldrPrDem |  -0.0063   -0.1351    0.0246   -0.1922 |      0.9405  
      ElecldrNot |  -0.0516    0.3624   -0.3655    0.3031 |      0.6266  
       Elecldr1C |   0.3455    0.1100    0.2056   -0.1319 |      0.8255  
       Elecldr1F |   0.1106   -0.0520    0.0314    0.0774 |      0.9790  
     ElecldrMLeg |   0.0892   -0.1404   -0.0947   -0.1080 |      0.9414  
    ElecldrMExec |   0.1842   -0.0165    0.0544   -0.1398 |      0.9399  
    legnoms_in~t |  -0.0302   -0.0013   -0.0672    0.3412 |      0.8747  
    legnoms_veto |   0.2788   -0.0235    0.1659   -0.2137 |      0.8437  
    legnoms_no~o |  -0.1513   -0.2300   -0.1200   -0.1566 |      0.8957  
    legnoms_pr~m |   0.0049   -0.0904    0.0033   -0.1662 |      0.9615  
      LdrexHighR |  -0.0146    0.7899   -0.0754   -0.1460 |      0.3739  
       LdrexLowR |   0.0547    0.3582    0.1970   -0.0522 |      0.8265  
      LdrexRebel |   0.1583   -0.1639    0.1386    0.8323 |      0.2856  
      LdrexDemEl |   0.1909   -0.3131    0.0392   -0.4301 |      0.6291  
      LdrexParty |   0.2493   -0.2792   -0.4598   -0.0355 |      0.5811  
      LdrexLoyal |   0.0476   -0.0075   -0.0153   -0.0704 |      0.9918  
      LdrexReltv |   0.0601   -0.0120    0.1594   -0.0056 |      0.9725  
     LdrexRulFam |  -0.7094   -0.4570    0.2524    0.0118 |      0.3173  
      LdrexOther |  -0.1031   -0.0764    0.0396   -0.0988 |      0.9742  
    partye~mpers |   0.5261    0.1467    0.4377    0.0198 |      0.5721  
    partyexcom~n |   0.2550   -0.1151   -0.3039    0.1236 |      0.7857  
    partyexcom~e |   0.1530   -0.1269   -0.2694   -0.0086 |      0.8680  
     createparty |   0.2653    0.3381    0.4181   -0.0196 |      0.6696  
    ---------------------------------------------------------------------

Factor rotation matrix

    --------------------------------------------------
                 | Factor1  Factor2  Factor3  Factor4 
    -------------+------------------------------------
         Factor1 |  0.7919  -0.6810  -0.3516   0.0068 
         Factor2 |  0.5524   0.7228  -0.0743   0.0866 
         Factor3 |  0.2367  -0.1162   0.9329  -0.0275 
         Factor4 |  0.1086  -0.0171   0.0221  -0.9958 
    --------------------------------------------------

.                 predict pcg1 pcg2 pcg3 pcg4     
(regression scoring assumed)

Scoring coefficients (method = regression; based on promax(3) rotated factors)

    ------------------------------------------------------
        Variable |  Factor1   Factor2   Factor3   Factor4 
    -------------+----------------------------------------
    partyrbrstmp |  0.02680   0.01576   0.03670  -0.01640 
       militrank |  0.02242   0.02956  -0.05618  -0.01428 
     ldrrotation | -0.00118   0.02256  -0.03038  -0.00120 
      milconsult | -0.01404   0.02914  -0.04609   0.00045 
    milmerit_mil | -0.02732   0.00270  -0.12508  -0.07334 
    milmeritpers |  0.00354   0.01162   0.09938   0.00347 
      milnotrial |  0.00596   0.00675   0.02771   0.00812 
      plebiscite |  0.00890  -0.01237   0.02469  -0.00812 
        heirclan |  0.00582  -0.00953   0.02278   0.00444 
      officepers |  0.01830  -0.01173   0.06865  -0.01675 
     paramilpers | -0.00920  -0.00698   0.05913  -0.00606 
    ParamilParty |  0.01865  -0.00387  -0.03306  -0.01447 
     ParamilFReb | -0.00675   0.01098  -0.02582   0.00811 
    supportparty |  0.00000   0.00000   0.00000   0.00000 
     partyleader |  0.00462  -0.01284   0.01368  -0.00756 
     localorgzns |  0.06588  -0.00944  -0.02184   0.01085 
       partymins |  0.01760  -0.03355  -0.01501   0.02727 
       excomcivn |  0.03000   0.00634  -0.02025  -0.01008 
     multiethnic |  0.13230  -0.08408  -0.03127  -0.01095 
      monoethnic |  0.08530  -0.05860   0.05429   0.05320 
       heirparty |  0.04830  -0.05116  -0.09351   0.00875 
      heirfamily | -0.00750   0.00463   0.09208   0.00245 
      legcompetn |  0.02290  -0.02727   0.00842  -0.08385 
    leaderrela~s |  0.00076  -0.01409   0.05405   0.00041 
       leaderciv |  0.00000   0.00000   0.00000   0.00000 
       leadermil |  0.04038   0.38889   0.08347   0.09343 
     leaderrebel | -0.01304   0.06313   0.02409   0.24506 
         heirciv |  0.02960  -0.01510  -0.06062  -0.00829 
          cabciv |  0.01311  -0.05238   0.00680  -0.02431 
          cabmil |  0.00642   0.09099  -0.02457  -0.00151 
      partymilit |  0.02306  -0.00667  -0.03864   0.04292 
       ldrPriorD |  0.06362  -0.13689   0.02416  -0.11053 
        ldrParty |  0.06377  -0.15358  -0.07298   0.00229 
          ldrMil |  0.00000   0.00000   0.00000   0.00000 
        ldrRebel |  0.05706  -0.10114   0.03911   0.19997 
          ldrCiv |  0.00823  -0.04575   0.00285  -0.02894 
        ldrOther |  0.01351  -0.06521   0.04264  -0.00043 
        ldrForgn |  0.04983  -0.11001  -0.01091   0.01235 
        ldrHered | -0.06771  -0.14847   0.02911   0.05652 
        SeizCoup | -0.00537   0.02925  -0.01457  -0.02738 
       SeizRebel |  0.00761  -0.00420  -0.05012   0.09639 
       SeizUpris |  0.00692  -0.00910  -0.01173  -0.00171 
        SeizElec |  0.01543  -0.03212   0.00965  -0.04422 
        SeizSucc |  0.00282  -0.01307   0.00445  -0.00332 
         SeizFam | -0.01139  -0.01582  -0.00425  -0.00893 
     PartyhNoWin | -0.02774  -0.00405  -0.00431   0.00648 
       PartyhWin | -0.03503   0.00249   0.00079  -0.02329 
       PartyhReb | -0.05011  -0.02603  -0.07901   0.09066 
    PartyhPrio~m | -0.03209  -0.03221  -0.03670  -0.12319 
    PartyhNopa~y | -0.52241  -0.06518  -0.12991  -0.00194 
      PartyhElec | -0.03472  -0.00113   0.01192  -0.00587 
    MilPartyAlly | -0.00820  -0.03303   0.00710   0.00794 
      MilPartyNo | -0.02791  -0.02535  -0.03578  -0.01381 
    MilPartyPr~r | -0.01820  -0.02794  -0.00193   0.01344 
      nomilitary | -0.02281  -0.04046   0.02669  -0.04882 
    milethnic_~e |  0.00000   0.00000   0.00000   0.00000 
    milethnic~ro | -0.04201  -0.04765   0.09881   0.03261 
    milethnic~mo | -0.01552   0.00089   0.09186   0.02810 
    sectyapp_p~y | -0.00235  -0.01219  -0.05687   0.04535 
    sectyapppers |  0.00133  -0.01589   0.12534   0.00167 
    ElecldrPrD~t | -0.01661   0.02733   0.00963   0.01175 
    ElecldrPrDem | -0.00777   0.03192   0.01205  -0.02936 
      ElecldrNot | -0.05137   0.22587  -0.08516   0.16809 
       Elecldr1C |  0.02539   0.17392   0.05127   0.00010 
       Elecldr1F |  0.00269   0.06506   0.00986   0.04369 
     ElecldrMLeg | -0.01756   0.05750  -0.01939   0.00773 
    ElecldrMExec | -0.00553   0.12705   0.02321   0.00317 
    legnoms_in~t | -0.01363  -0.00016  -0.00780   0.01764 
    legnoms_veto |  0.03174   0.00419   0.04288  -0.04324 
    legnoms_no~o | -0.02634  -0.02338  -0.01627  -0.00760 
    legnoms_pr~m | -0.00831  -0.00811   0.00314  -0.00444 
      LdrexHighR |  0.00000   0.00000   0.00000   0.00000 
       LdrexLowR |  0.02591  -0.06309   0.03133  -0.00004 
      LdrexRebel | -0.01023  -0.13420   0.03297   0.23924 
      LdrexDemEl |  0.02648  -0.17598   0.01525  -0.04650 
      LdrexParty |  0.03310  -0.20851  -0.08367   0.08240 
      LdrexLoyal | -0.00227  -0.04495   0.00342  -0.00330 
      LdrexReltv | -0.00655  -0.06428   0.04544   0.03471 
     LdrexRulFam | -0.12751  -0.11320   0.11726   0.10435 
      LdrexOther | -0.01230  -0.06881   0.00727   0.00479 
    partye~mpers |  0.05330   0.01358   0.13711  -0.01283 
    partyexcom~n | -0.01157   0.00086  -0.03160   0.02215 
    partyexcom~e | -0.01887  -0.01305  -0.02888  -0.00435 
     createparty | -0.07612  -0.04241   0.05629  -0.00888 
    ------------------------------------------------------


.                 factor $allvar1 $allvar2 $allvar3 $allvar4 $allvar5 $allvar6 $allvar
> 7 $allvar8 $allvar9 $allvar10 cg_gparties cg_party lparty cg_ginst cg_mil, factors(4
> )               
(obs=3,364)
(collinear variables specified)

Factor analysis/correlation                      Number of obs    =      3,364
    Method: principal factors                    Retained factors =          4
    Rotation: (unrotated)                        Number of params =        350

    --------------------------------------------------------------------------
         Factor  |   Eigenvalue   Difference        Proportion   Cumulative
    -------------+------------------------------------------------------------
        Factor1  |     11.37724      4.76561            0.1572       0.1572
        Factor2  |      6.61163      0.74671            0.0914       0.2486
        Factor3  |      5.86492      0.93151            0.0810       0.3296
        Factor4  |      4.93341      1.50094            0.0682       0.3978
        Factor5  |      3.43247      0.17900            0.0474       0.4452
        Factor6  |      3.25347      0.56510            0.0450       0.4901
        Factor7  |      2.68837      0.40028            0.0371       0.5273
        Factor8  |      2.28809      0.05736            0.0316       0.5589
        Factor9  |      2.23073      0.25530            0.0308       0.5897
       Factor10  |      1.97542      0.13856            0.0273       0.6170
       Factor11  |      1.83686      0.20034            0.0254       0.6424
       Factor12  |      1.63653      0.06401            0.0226       0.6650
       Factor13  |      1.57251      0.06365            0.0217       0.6867
       Factor14  |      1.50887      0.09167            0.0208       0.7076
       Factor15  |      1.41720      0.08524            0.0196       0.7272
       Factor16  |      1.33196      0.04823            0.0184       0.7456
       Factor17  |      1.28373      0.10012            0.0177       0.7633
       Factor18  |      1.18360      0.05314            0.0164       0.7797
       Factor19  |      1.13046      0.05030            0.0156       0.7953
       Factor20  |      1.08016      0.02507            0.0149       0.8102
       Factor21  |      1.05509      0.06713            0.0146       0.8248
       Factor22  |      0.98795      0.00838            0.0137       0.8384
       Factor23  |      0.97957      0.08757            0.0135       0.8520
       Factor24  |      0.89200      0.01423            0.0123       0.8643
       Factor25  |      0.87776      0.04586            0.0121       0.8764
       Factor26  |      0.83190      0.05035            0.0115       0.8879
       Factor27  |      0.78155      0.04277            0.0108       0.8987
       Factor28  |      0.73879      0.03524            0.0102       0.9089
       Factor29  |      0.70354      0.04700            0.0097       0.9187
       Factor30  |      0.65654      0.02918            0.0091       0.9277
       Factor31  |      0.62736      0.03948            0.0087       0.9364
       Factor32  |      0.58788      0.04442            0.0081       0.9445
       Factor33  |      0.54346      0.03339            0.0075       0.9520
       Factor34  |      0.51008      0.03890            0.0070       0.9591
       Factor35  |      0.47118      0.02824            0.0065       0.9656
       Factor36  |      0.44294      0.02809            0.0061       0.9717
       Factor37  |      0.41485      0.05878            0.0057       0.9774
       Factor38  |      0.35606      0.01986            0.0049       0.9824
       Factor39  |      0.33620      0.01782            0.0046       0.9870
       Factor40  |      0.31838      0.01949            0.0044       0.9914
       Factor41  |      0.29889      0.03069            0.0041       0.9955
       Factor42  |      0.26819      0.01917            0.0037       0.9992
       Factor43  |      0.24903      0.02475            0.0034       1.0027
       Factor44  |      0.22428      0.01307            0.0031       1.0058
       Factor45  |      0.21120      0.01990            0.0029       1.0087
       Factor46  |      0.19130      0.01997            0.0026       1.0113
       Factor47  |      0.17133      0.02829            0.0024       1.0137
       Factor48  |      0.14304      0.01597            0.0020       1.0157
       Factor49  |      0.12707      0.00824            0.0018       1.0174
       Factor50  |      0.11883      0.01423            0.0016       1.0191
       Factor51  |      0.10461      0.00890            0.0014       1.0205
       Factor52  |      0.09571      0.02420            0.0013       1.0219
       Factor53  |      0.07151      0.00348            0.0010       1.0228
       Factor54  |      0.06802      0.01360            0.0009       1.0238
       Factor55  |      0.05442      0.01378            0.0008       1.0245
       Factor56  |      0.04064      0.00701            0.0006       1.0251
       Factor57  |      0.03363      0.00738            0.0005       1.0256
       Factor58  |      0.02625      0.00737            0.0004       1.0259
       Factor59  |      0.01888      0.00846            0.0003       1.0262
       Factor60  |      0.01042      0.00979            0.0001       1.0263
       Factor61  |      0.00063      0.00063            0.0000       1.0263
       Factor62  |      0.00000      0.00000            0.0000       1.0263
       Factor63  |      0.00000      0.00000            0.0000       1.0263
       Factor64  |     -0.00000      0.00000           -0.0000       1.0263
       Factor65  |     -0.00000      0.00000           -0.0000       1.0263
       Factor66  |     -0.00000      0.00221           -0.0000       1.0263
       Factor67  |     -0.00221      0.00010           -0.0000       1.0263
       Factor68  |     -0.00231      0.00557           -0.0000       1.0263
       Factor69  |     -0.00788      0.00445           -0.0001       1.0262
       Factor70  |     -0.01234      0.00455           -0.0002       1.0260
       Factor71  |     -0.01689      0.01013           -0.0002       1.0258
       Factor72  |     -0.02702      0.01373           -0.0004       1.0254
       Factor73  |     -0.04076      0.00325           -0.0006       1.0248
       Factor74  |     -0.04401      0.00847           -0.0006       1.0242
       Factor75  |     -0.05248      0.01376           -0.0007       1.0235
       Factor76  |     -0.06624      0.01099           -0.0009       1.0226
       Factor77  |     -0.07722      0.00432           -0.0011       1.0215
       Factor78  |     -0.08155      0.01175           -0.0011       1.0204
       Factor79  |     -0.09330      0.01094           -0.0013       1.0191
       Factor80  |     -0.10424      0.00564           -0.0014       1.0177
       Factor81  |     -0.10988      0.00409           -0.0015       1.0161
       Factor82  |     -0.11397      0.00897           -0.0016       1.0146
       Factor83  |     -0.12294      0.00085           -0.0017       1.0129
       Factor84  |     -0.12380      0.01368           -0.0017       1.0111
       Factor85  |     -0.13748      0.00745           -0.0019       1.0093
       Factor86  |     -0.14493      0.01336           -0.0020       1.0072
       Factor87  |     -0.15829      0.01089           -0.0022       1.0051
       Factor88  |     -0.16918      0.02791           -0.0023       1.0027
       Factor89  |     -0.19709            .           -0.0027       1.0000
    --------------------------------------------------------------------------
    LR test: independent vs. saturated: chi2(3916)=       . Prob>chi2 =      .

Factor loadings (pattern matrix) and unique variances

    ---------------------------------------------------------------------
        Variable |  Factor1   Factor2   Factor3   Factor4 |   Uniqueness 
    -------------+----------------------------------------+--------------
    partyrbrstmp |  -0.0603    0.4648    0.3116    0.0500 |      0.6808  
       militrank |  -0.7413    0.2774   -0.1270    0.3921 |      0.2037  
     ldrrotation |  -0.2230   -0.0263   -0.1351    0.2502 |      0.8687  
      milconsult |  -0.4961   -0.0389   -0.2895    0.2838 |      0.5880  
    milmerit_mil |   0.1595   -0.3876   -0.2786    0.3343 |      0.6349  
    milmeritpers |  -0.1914    0.4195    0.1767   -0.2847 |      0.6751  
      milnotrial |  -0.2587    0.4217    0.1255   -0.3193 |      0.6375  
      plebiscite |  -0.1016    0.1805    0.3143    0.2254 |      0.8075  
        heirclan |  -0.0530    0.1095    0.1315   -0.3759 |      0.8266  
      officepers |  -0.1936    0.2205    0.5371   -0.2598 |      0.5579  
     paramilpers |  -0.2117    0.1958    0.4105   -0.2213 |      0.6994  
    ParamilParty |   0.3346    0.0714   -0.1418    0.2053 |      0.8207  
     ParamilFReb |  -0.1120    0.0052   -0.2571    0.0100 |      0.9212  
    supportparty |   0.5703    0.7277    0.1206    0.1476 |      0.1088  
     partyleader |   0.4521    0.4455    0.0365   -0.0163 |      0.5955  
     localorgzns |   0.5943    0.6256    0.0612    0.1719 |      0.2222  
       partymins |   0.5801    0.5038   -0.0612    0.1047 |      0.3949  
       excomcivn |   0.6050    0.3703   -0.0265    0.1377 |      0.4772  
     multiethnic |   0.4236    0.2574   -0.0368    0.2654 |      0.6825  
      monoethnic |  -0.0153    0.3005    0.1399   -0.1795 |      0.8577  
       heirparty |   0.7156    0.0893   -0.2420    0.2074 |      0.3784  
      heirfamily |  -0.3679    0.0377    0.3712   -0.4110 |      0.5565  
      legcompetn |   0.2815   -0.0571    0.4802    0.2289 |      0.6345  
    leaderrela~s |  -0.1149    0.1129    0.3368   -0.3221 |      0.7569  
       leaderciv |   0.6771   -0.5090    0.2871    0.0844 |      0.1930  
       leadermil |  -0.8032    0.3331   -0.0691    0.3695 |      0.1027  
     leaderrebel |   0.1381    0.2990   -0.3455   -0.6818 |      0.3073  
         heirciv |   0.7556   -0.1832   -0.0949    0.1329 |      0.3689  
          cabciv |   0.4928   -0.2444    0.2468   -0.1599 |      0.6109  
          cabmil |  -0.5147    0.2422   -0.2801    0.1704 |      0.5689  
      partymilit |   0.5341    0.1509   -0.4420   -0.0744 |      0.4910  
       ldrPriorD |   0.2573   -0.1619    0.4097    0.0988 |      0.7300  
        ldrParty |   0.4954   -0.0940   -0.2097    0.2739 |      0.6268  
          ldrMil |  -0.7434    0.2102   -0.0851    0.3403 |      0.2802  
        ldrRebel |   0.1073    0.2558   -0.2992   -0.6172 |      0.4526  
          ldrCiv |   0.0760   -0.0154    0.1027    0.0694 |      0.9786  
        ldrOther |  -0.0862    0.0276    0.1971   -0.1453 |      0.9319  
        ldrForgn |   0.1306    0.0169   -0.0098   -0.0757 |      0.9768  
        ldrHered |  -0.1634   -0.4706    0.2060   -0.3315 |      0.5996  
        SeizCoup |  -0.7067    0.1887    0.0107    0.3104 |      0.3686  
       SeizRebel |   0.2724    0.2038   -0.4755   -0.4268 |      0.4760  
       SeizUpris |  -0.0103   -0.1006   -0.0390    0.0735 |      0.9829  
        SeizElec |   0.3352   -0.1778    0.3912    0.1819 |      0.6699  
        SeizSucc |  -0.0024    0.0738    0.1499   -0.0254 |      0.9714  
         SeizFam |  -0.1157   -0.3752    0.1385   -0.2517 |      0.7633  
     PartyhNoWin |   0.0632    0.2063   -0.0746   -0.0558 |      0.9447  
       PartyhWin |   0.0606    0.0063    0.1161    0.0040 |      0.9828  
       PartyhReb |   0.2921    0.1691   -0.4972   -0.2704 |      0.5658  
    PartyhPrio~m |   0.3905   -0.0336    0.2845    0.2547 |      0.7005  
    PartyhNopa~y |  -0.5703   -0.7277   -0.1206   -0.1476 |      0.1088  
      PartyhElec |  -0.0099   -0.0074    0.1386   -0.0520 |      0.9779  
    MilPartyAlly |  -0.1715    0.1650    0.0077    0.1616 |      0.9172  
      MilPartyNo |  -0.5496   -0.3497   -0.3038    0.1178 |      0.4694  
    MilPartyPr~r |  -0.1879    0.3190   -0.0522    0.2432 |      0.8011  
      nomilitary |   0.1185   -0.1552    0.1866    0.0228 |      0.9265  
    milethnic_~e |   0.0634   -0.0209   -0.2908    0.1760 |      0.8800  
    milethnic~ro |   0.0250   -0.0449    0.1621   -0.1074 |      0.9595  
    milethnic~mo |  -0.1765    0.1609    0.1043   -0.1165 |      0.9185  
    sectyapp_p~y |   0.4228   -0.0590   -0.4701    0.1380 |      0.5777  
    sectyapppers |  -0.1539    0.2543    0.4937   -0.2878 |      0.5850  
    ElecldrPrD~t |   0.0454   -0.0268    0.0450   -0.0187 |      0.9948  
    ElecldrPrDem |   0.0787   -0.1579    0.1881   -0.0019 |      0.9335  
      ElecldrNot |  -0.1115   -0.0065   -0.6202   -0.0800 |      0.5964  
       Elecldr1C |  -0.0042    0.2882    0.2282    0.1043 |      0.8540  
       Elecldr1F |   0.0124    0.1033   -0.0423   -0.1618 |      0.9612  
     ElecldrMLeg |   0.2054   -0.0890    0.1093    0.1031 |      0.9273  
    ElecldrMExec |   0.0534    0.0116    0.2645    0.1805 |      0.8945  
    legnoms_in~t |   0.0095    0.0631   -0.3350   -0.2185 |      0.8360  
    legnoms_veto |   0.0582    0.1359    0.3687    0.2079 |      0.7990  
    legnoms_no~o |   0.1560   -0.3013    0.1071    0.0647 |      0.8692  
    legnoms_pr~m |   0.0551   -0.1401    0.1457    0.0284 |      0.9553  
      LdrexHighR |  -0.6606    0.2214   -0.1533    0.3954 |      0.3347  
       LdrexLowR |  -0.3194    0.2118    0.1109    0.0426 |      0.8390  
      LdrexRebel |   0.1336    0.2683   -0.3275   -0.7039 |      0.3075  
      LdrexDemEl |   0.3495   -0.1734    0.4088    0.1313 |      0.6634  
      LdrexParty |   0.5396   -0.1173   -0.2218    0.2166 |      0.5989  
      LdrexLoyal |   0.0376   -0.0088    0.0584    0.0739 |      0.9896  
      LdrexReltv |  -0.0248    0.0611    0.1488   -0.0868 |      0.9660  
     LdrexRulFam |  -0.1681   -0.4910    0.2118   -0.3512 |      0.5625  
      LdrexOther |  -0.0597   -0.1994    0.0735   -0.0884 |      0.9434  
    partye~mpers |  -0.0529    0.5437    0.2771   -0.1003 |      0.6148  
    partyexcom~n |   0.3408    0.0041   -0.2191    0.1150 |      0.8226  
    partyexcom~e |   0.2883   -0.0644   -0.1315    0.1382 |      0.8763  
     createparty |  -0.3418    0.3585    0.2439    0.0018 |      0.6951  
     cg_gparties |  -0.0664   -0.2244    0.3206    0.1829 |      0.8090  
        cg_party |   0.3047    0.1657    0.1748    0.0172 |      0.8489  
          lparty |   0.4888    0.5508    0.0442    0.1521 |      0.4326  
    cg_ginstit~s |   0.2996   -0.0256    0.4327    0.1920 |      0.6855  
          cg_mil |  -0.7216    0.4221   -0.1081    0.2041 |      0.2478  
    ---------------------------------------------------------------------

.                 rotate, promax(3) 

Factor analysis/correlation                      Number of obs    =      3,364
    Method: principal factors                    Retained factors =          4
    Rotation: oblique promax (Kaiser off)        Number of params =        350

    --------------------------------------------------------------------------
         Factor  |     Variance   Proportion    Rotated factors are correlated
    -------------+------------------------------------------------------------
        Factor1  |      9.71580       0.1342
        Factor2  |      8.47537       0.1171
        Factor3  |      6.58859       0.0910
        Factor4  |      5.53743       0.0765
    --------------------------------------------------------------------------
    LR test: independent vs. saturated: chi2(3916)=       . Prob>chi2 =      .

Rotated factor loadings (pattern matrix) and unique variances

    ---------------------------------------------------------------------
        Variable |  Factor1   Factor2   Factor3   Factor4 |   Uniqueness 
    -------------+----------------------------------------+--------------
    partyrbrstmp |   0.2208    0.4085    0.3670    0.0843 |      0.6808  
       militrank |   0.9046   -0.0014   -0.0039    0.2084 |      0.2037  
     ldrrotation |   0.3135   -0.0621   -0.1805    0.1487 |      0.8687  
      milconsult |   0.5574   -0.2023   -0.2359    0.1079 |      0.5880  
    milmerit_mil |  -0.0543   -0.1780   -0.5598    0.2223 |      0.6349  
    milmeritpers |   0.1449    0.1978    0.4777   -0.2448 |      0.6751  
      milnotrial |   0.1878    0.1541    0.4799   -0.2987 |      0.6375  
      plebiscite |   0.2037    0.1978    0.1921    0.3105 |      0.8075  
        heirclan |  -0.1438   -0.0340    0.3549   -0.2641 |      0.8266  
      officepers |  -0.0286    0.0597    0.6629    0.0279 |      0.5579  
     paramilpers |   0.0245    0.0324    0.5466   -0.0019 |      0.6994  
    ParamilParty |  -0.0557    0.2695   -0.2915    0.0554 |      0.8207  
     ParamilFReb |   0.1516   -0.0638   -0.1586   -0.1285 |      0.9212  
    supportparty |   0.0044    0.9456    0.0748   -0.0423 |      0.1088  
     partyleader |  -0.1183    0.5926    0.0477   -0.1383 |      0.5955  
     localorgzns |  -0.0350    0.8721   -0.0219   -0.0274 |      0.2222  
       partymins |  -0.0906    0.7319   -0.1091   -0.1147 |      0.3949  
       excomcivn |  -0.1650    0.6413   -0.1524   -0.0326 |      0.4772  
     multiethnic |  -0.0208    0.4968   -0.2142    0.1061 |      0.6825  
      monoethnic |   0.0273    0.2072    0.3028   -0.1537 |      0.8577  
       heirparty |  -0.2904    0.4571   -0.4710   -0.0200 |      0.3784  
      heirfamily |  -0.0352   -0.2365    0.6175   -0.1275 |      0.5565  
      legcompetn |  -0.2245    0.1867    0.1225    0.4527 |      0.6345  
    leaderrela~s |  -0.1207   -0.0290    0.4937   -0.1060 |      0.7569  
       leaderciv |  -0.7569   -0.0730   -0.1994    0.3367 |      0.1930  
       leadermil |   0.9492    0.0148    0.0868    0.2088 |      0.1027  
     leaderrebel |  -0.2309    0.0920    0.1822   -0.8367 |      0.3073  
         heirciv |  -0.5301    0.2303   -0.4222    0.0734 |      0.3689  
          cabciv |  -0.6174   -0.0085    0.0458    0.0493 |      0.6109  
          cabmil |   0.6458   -0.0030   -0.0702   -0.0573 |      0.5689  
      partymilit |  -0.2331    0.3258   -0.3879   -0.3688 |      0.4910  
       ldrPriorD |  -0.3114    0.0412    0.1165    0.3379 |      0.7300  
        ldrParty |  -0.1971    0.2186   -0.4793    0.1133 |      0.6268  
          ldrMil |   0.8346   -0.0726    0.0342    0.2077 |      0.2802  
        ldrRebel |  -0.2073    0.0632    0.1752   -0.7450 |      0.4526  
          ldrCiv |  -0.0497    0.0508    0.0093    0.1138 |      0.9786  
        ldrOther |  -0.0530   -0.0460    0.2581   -0.0151 |      0.9319  
        ldrForgn |  -0.1224    0.0524    0.0021   -0.0780 |      0.9768  
        ldrHered |  -0.3465   -0.5651    0.2294   -0.0203 |      0.5996  
        SeizCoup |   0.7579   -0.0758    0.1028    0.2398 |      0.3686  
       SeizRebel |  -0.2031    0.1402   -0.1237   -0.6785 |      0.4760  
       SeizUpris |   0.0070   -0.0720   -0.0986    0.0674 |      0.9829  
        SeizElec |  -0.3250    0.0877    0.0291    0.3967 |      0.6699  
        SeizSucc |  -0.0131    0.0656    0.1478    0.0406 |      0.9714  
         SeizFam |  -0.2733   -0.4418    0.1529   -0.0206 |      0.7633  
     PartyhNoWin |   0.0456    0.1847    0.0247   -0.1468 |      0.9447  
       PartyhWin |  -0.0667    0.0436    0.0667    0.0621 |      0.9828  
       PartyhReb |  -0.1445    0.1650   -0.2430   -0.5535 |      0.5658  
    PartyhPrio~m |  -0.2272    0.2515   -0.0586    0.3554 |      0.7005  
    PartyhNopa~y |  -0.0044   -0.9456   -0.0748    0.0423 |      0.1088  
      PartyhElec |  -0.0594   -0.0165    0.1303    0.0356 |      0.9779  
    MilPartyAlly |   0.2879    0.1102    0.0194    0.0984 |      0.9172  
      MilPartyNo |   0.3562   -0.5451   -0.2392    0.0533 |      0.4694  
    MilPartyPr~r |   0.4344    0.2548   -0.0149    0.0904 |      0.8011  
      nomilitary |  -0.1949   -0.0572    0.0387    0.1584 |      0.9265  
    milethnic_~e |   0.1124    0.0434   -0.3346   -0.0117 |      0.8800  
    milethnic~ro |  -0.1383   -0.0472    0.1558    0.0119 |      0.9595  
    milethnic~mo |   0.1155    0.0280    0.2439   -0.0752 |      0.9185  
    sectyapp_p~y |  -0.1365    0.1549   -0.5613   -0.1467 |      0.5777  
    sectyapppers |  -0.0442    0.0958    0.6461   -0.0299 |      0.5850  
    ElecldrPrD~t |  -0.0667   -0.0041    0.0213    0.0146 |      0.9948  
    ElecldrPrDem |  -0.1817   -0.0856    0.0642    0.1415 |      0.9335  
      ElecldrNot |   0.1880   -0.1271   -0.3771   -0.3973 |      0.5964  
       Elecldr1C |   0.1441    0.2934    0.2028    0.1298 |      0.8540  
       Elecldr1F |  -0.0344    0.0429    0.0885   -0.1849 |      0.9612  
     ElecldrMLeg |  -0.1612    0.0588   -0.0661    0.1596 |      0.9273  
    ElecldrMExec |  -0.0011    0.1084    0.0809    0.2865 |      0.8945  
    legnoms_in~t |  -0.0093   -0.0312   -0.1054   -0.3799 |      0.8360  
    legnoms_veto |   0.0454    0.2332    0.1804    0.3310 |      0.7990  
    legnoms_no~o |  -0.2510   -0.1583   -0.1005    0.1883 |      0.8692  
    legnoms_pr~m |  -0.1293   -0.0753    0.0291    0.1393 |      0.9553  
      LdrexHighR |   0.8282   -0.0126   -0.0666    0.2087 |      0.3347  
       LdrexLowR |   0.3257    0.0528    0.2187    0.0511 |      0.8390  
      LdrexRebel |  -0.2593    0.0582    0.1989   -0.8361 |      0.3075  
      LdrexDemEl |  -0.3647    0.0843    0.0672    0.3631 |      0.6634  
      LdrexParty |  -0.2678    0.2013   -0.4768    0.0642 |      0.5989  
      LdrexLoyal |  -0.0057    0.0365   -0.0122    0.0932 |      0.9896  
      LdrexReltv |  -0.0363    0.0257    0.1835   -0.0057 |      0.9660  
     LdrexRulFam |  -0.3653   -0.5904    0.2393   -0.0274 |      0.5625  
      LdrexOther |  -0.1221   -0.2204    0.0556    0.0261 |      0.9434  
    partye~mpers |   0.1822    0.4321    0.4484   -0.0800 |      0.6148  
    partyexcom~n |  -0.1225    0.1820   -0.3214   -0.0422 |      0.8226  
    partyexcom~e |  -0.1286    0.1119   -0.2772    0.0461 |      0.8763  
     createparty |   0.3588    0.1657    0.3921    0.0504 |      0.6951  
     cg_gparties |  -0.0455   -0.1456    0.0792    0.3904 |      0.8090  
        cg_party |  -0.1685    0.3032    0.0825    0.0494 |      0.8489  
          lparty |  -0.0036    0.7512   -0.0167   -0.0272 |      0.4326  
    cg_ginstit~s |  -0.2297    0.2077    0.1133    0.3869 |      0.6855  
          cg_mil |   0.8560    0.0770    0.1549    0.0236 |      0.2478  
    ---------------------------------------------------------------------

Factor rotation matrix

    --------------------------------------------------
                 | Factor1  Factor2  Factor3  Factor4 
    -------------+------------------------------------
         Factor1 | -0.8318   0.6399  -0.3877   0.0504 
         Factor2 |  0.3824   0.7301   0.2968  -0.3180 
         Factor3 | -0.2912   0.0987   0.7606   0.6698 
         Factor4 |  0.2778   0.2185  -0.4279   0.6691 
    --------------------------------------------------

.                 predict cg1 cg2 cg3 cg4 
(regression scoring assumed)

Scoring coefficients (method = regression; based on promax(3) rotated factors)

    ------------------------------------------------------
        Variable |  Factor1   Factor2   Factor3   Factor4 
    -------------+----------------------------------------
    partyrbrstmp |  0.01355   0.02412   0.02742   0.00611 
       militrank |  0.03445   0.02487  -0.04784   0.01888 
     ldrrotation |  0.02632   0.00841  -0.02742   0.00290 
      milconsult |  0.03118  -0.01612  -0.04194  -0.00092 
    milmerit_mil |  0.00093  -0.03517  -0.10786   0.08030 
    milmeritpers |  0.01945   0.02657   0.08668  -0.00739 
      milnotrial | -0.00007  -0.00185   0.03247  -0.00661 
      plebiscite | -0.01045   0.01163   0.02640   0.01338 
        heirclan | -0.00428   0.01291   0.02445   0.00196 
      officepers | -0.01409   0.01659   0.07731   0.01429 
     paramilpers | -0.01356  -0.01914   0.06810   0.00326 
    ParamilParty | -0.00851   0.01340  -0.02880   0.01594 
     ParamilFReb |  0.00348  -0.01241  -0.02300  -0.00742 
    supportparty |  0.00000   0.00000   0.00000   0.00000 
     partyleader | -0.01315   0.00957   0.01007   0.00477 
     localorgzns |  0.00789   0.05943  -0.02580  -0.00201 
       partymins | -0.03089   0.03708  -0.01139  -0.02417 
       excomcivn |  0.00070   0.02040  -0.01684   0.00508 
     multiethnic | -0.17617   0.21517   0.01654   0.19269 
      monoethnic | -0.15916   0.15853   0.10006   0.10821 
       heirparty | -0.04800   0.05461  -0.07906  -0.00822 
      heirfamily |  0.00986   0.01887   0.08478  -0.01396 
      legcompetn | -0.05176   0.04178   0.03817   0.12706 
    leaderrela~s | -0.01316   0.00686   0.04786  -0.00257 
       leaderciv |  0.00000   0.00000   0.00000   0.00000 
       leadermil |  0.40345   0.07424   0.02018  -0.08084 
     leaderrebel |  0.06836   0.00121   0.03067  -0.24244 
         heirciv | -0.01038   0.06476  -0.07984   0.00021 
          cabciv | -0.05181   0.01325  -0.00661   0.03429 
          cabmil |  0.09919   0.00717  -0.04571  -0.01788 
      partymilit | -0.01077   0.02192  -0.03628  -0.03230 
       ldrPriorD | -0.12582   0.05168   0.02925   0.08099 
        ldrParty | -0.12258   0.08239  -0.08410   0.01395 
          ldrMil |  0.00000   0.00000   0.00000   0.00000 
        ldrRebel | -0.09697   0.06750   0.03441  -0.17421 
          ldrCiv | -0.04306   0.01023   0.00352   0.03039 
        ldrOther | -0.06670   0.01207   0.05441  -0.00366 
        ldrForgn | -0.08683   0.04825  -0.02190  -0.02429 
        ldrHered | -0.07639  -0.10660   0.02814  -0.02746 
        SeizCoup |  0.05326  -0.00808  -0.03713   0.02501 
       SeizRebel |  0.00452   0.00720  -0.06143  -0.09869 
       SeizUpris |  0.00062   0.01884  -0.02016   0.00679 
        SeizElec | -0.03236   0.00830   0.01054   0.02781 
        SeizSucc | -0.01242   0.00682   0.00844   0.00300 
         SeizFam | -0.01625  -0.00145   0.00744  -0.00905 
     PartyhNoWin |  0.00629  -0.01323  -0.00899  -0.02108 
       PartyhWin | -0.00522  -0.02957   0.00928   0.02360 
       PartyhReb | -0.01119  -0.03587  -0.07390  -0.07898 
    PartyhPrio~m | -0.01788  -0.02439  -0.03742   0.08979 
    PartyhNopa~y | -0.13199  -0.43503  -0.03988   0.11466 
      PartyhElec | -0.00802  -0.03257   0.01810   0.00114 
    MilPartyAlly | -0.03587  -0.00061   0.01415  -0.00648 
      MilPartyNo | -0.02883  -0.04061  -0.01585   0.00610 
    MilPartyPr~r | -0.01939   0.00321   0.00186  -0.00954 
      nomilitary | -0.04277  -0.01885   0.03060   0.05853 
    milethnic_~e |  0.00000   0.00000   0.00000   0.00000 
    milethnic~ro | -0.04838  -0.01574   0.09741  -0.00555 
    milethnic~mo | -0.00330  -0.00671   0.08844  -0.02000 
    sectyapp_p~y | -0.01146   0.00473  -0.05996  -0.04295 
    sectyapppers | -0.01562   0.01197   0.11705  -0.00107 
    ElecldrPrD~t |  0.02970  -0.01210  -0.00960  -0.01718 
    ElecldrPrDem |  0.04830  -0.01280  -0.00107   0.01456 
      ElecldrNot |  0.26308  -0.05295  -0.16621  -0.20317 
       Elecldr1C |  0.23287   0.01643  -0.02538  -0.02551 
       Elecldr1F |  0.09539  -0.00106  -0.01445  -0.07857 
     ElecldrMLeg |  0.08689  -0.02033  -0.04559  -0.00557 
    ElecldrMExec |  0.17264  -0.01020  -0.02487   0.01887 
    legnoms_in~t | -0.00828  -0.00930  -0.00733  -0.01211 
    legnoms_veto |  0.00744   0.02589   0.03624   0.04063 
    legnoms_no~o | -0.01823  -0.03231  -0.01955  -0.00572 
    legnoms_pr~m | -0.01228  -0.02594   0.00436  -0.00200 
      LdrexHighR |  0.21457  -0.04259   0.06774   0.07488 
       LdrexLowR |  0.07751  -0.00191   0.08244   0.04158 
      LdrexRebel |  0.01759  -0.03626   0.09422  -0.14523 
      LdrexDemEl | -0.02380  -0.00952   0.08829   0.09867 
      LdrexParty |  0.00000   0.00000   0.00000   0.00000 
      LdrexLoyal |  0.01526  -0.00781   0.02817   0.03801 
      LdrexReltv |  0.00220  -0.02582   0.07897  -0.00586 
     LdrexRulFam |  0.01899  -0.12233   0.11121  -0.00271 
      LdrexOther | -0.01067  -0.05879   0.02993   0.00769 
    partye~mpers |  0.01695   0.06213   0.12328  -0.01240 
    partyexcom~n |  0.00019   0.00102  -0.02659  -0.01274 
    partyexcom~e | -0.00842  -0.00360  -0.02390   0.00628 
     createparty | -0.04720  -0.06736   0.06024   0.00088 
     cg_gparties | -0.03086  -0.01733   0.01591   0.07292 
        cg_party | -0.00632   0.03025   0.00719   0.00832 
          lparty | -0.01093   0.03590   0.00380   0.00832 
    cg_ginstit~s | -0.01127   0.01433   0.02151   0.03788 
          cg_mil |  0.00148  -0.01762   0.02286   0.00466 
    ------------------------------------------------------


.                 pwcorr pr1 pr2 pr3 pr4 pcg1 pcg2 pcg3 pcg4

             |      pr1      pr2      pr3      pr4     pcg1     pcg2     pcg3
-------------+---------------------------------------------------------------
         pr1 |   1.0000 
         pr2 |  -0.1649   1.0000 
         pr3 |  -0.1033   0.0773   1.0000 
         pr4 |  -0.0596   0.0733  -0.0525   1.0000 
        pcg1 |   0.9999  -0.1613  -0.1084  -0.0559   1.0000 
        pcg2 |  -0.1575   0.9999   0.0777   0.0763  -0.1539   1.0000 
        pcg3 |  -0.1052   0.0713   0.9998  -0.0549  -0.1103   0.0716   1.0000 
        pcg4 |  -0.0578   0.0666  -0.0520   0.9998  -0.0542   0.0696  -0.0544 

             |     pcg4
-------------+---------
        pcg4 |   1.0000 

.                 pwcorr pr1  cg2

             |      pr1      cg2
-------------+------------------
         pr1 |   1.0000 
         cg2 |   0.9927   1.0000 

.                 pwcorr pr2  cg1

             |      pr2      cg1
-------------+------------------
         pr2 |   1.0000 
         cg1 |   0.9954   1.0000 

.                 drop pcg* cg1 cg2 cg3 cg4

.                 * Svolik comparison *
.                 factor $allvar1 $allvar2 $allvar3 $allvar4 $allvar5 $allvar6 $allvar
> 7 $allvar8 $allvar9 $allvar10 if sv_parties~=., factors(4)
(obs=4,171)
(collinear variables specified)

Factor analysis/correlation                      Number of obs    =      4,171
    Method: principal factors                    Retained factors =          4
    Rotation: (unrotated)                        Number of params =        330

    --------------------------------------------------------------------------
         Factor  |   Eigenvalue   Difference        Proportion   Cumulative
    -------------+------------------------------------------------------------
        Factor1  |     10.94083      3.50096            0.1608       0.1608
        Factor2  |      7.43987      1.87819            0.1093       0.2701
        Factor3  |      5.56167      0.67648            0.0817       0.3519
        Factor4  |      4.88519      1.71116            0.0718       0.4237
        Factor5  |      3.17403      0.63923            0.0466       0.4703
        Factor6  |      2.53480      0.05437            0.0373       0.5076
        Factor7  |      2.48043      0.35241            0.0365       0.5440
        Factor8  |      2.12803      0.17194            0.0313       0.5753
        Factor9  |      1.95609      0.08507            0.0287       0.6040
       Factor10  |      1.87102      0.20941            0.0275       0.6315
       Factor11  |      1.66161      0.12129            0.0244       0.6559
       Factor12  |      1.54032      0.11875            0.0226       0.6786
       Factor13  |      1.42157      0.01743            0.0209       0.6995
       Factor14  |      1.40414      0.07960            0.0206       0.7201
       Factor15  |      1.32454      0.09008            0.0195       0.7396
       Factor16  |      1.23446      0.02422            0.0181       0.7577
       Factor17  |      1.21024      0.11233            0.0178       0.7755
       Factor18  |      1.09790      0.01987            0.0161       0.7916
       Factor19  |      1.07803      0.05485            0.0158       0.8075
       Factor20  |      1.02318      0.03602            0.0150       0.8225
       Factor21  |      0.98717      0.05872            0.0145       0.8370
       Factor22  |      0.92845      0.01847            0.0136       0.8507
       Factor23  |      0.90998      0.05404            0.0134       0.8640
       Factor24  |      0.85594      0.04048            0.0126       0.8766
       Factor25  |      0.81546      0.04992            0.0120       0.8886
       Factor26  |      0.76555      0.01469            0.0113       0.8999
       Factor27  |      0.75085      0.09434            0.0110       0.9109
       Factor28  |      0.65651      0.02265            0.0096       0.9205
       Factor29  |      0.63386      0.02203            0.0093       0.9299
       Factor30  |      0.61183      0.04413            0.0090       0.9388
       Factor31  |      0.56769      0.04880            0.0083       0.9472
       Factor32  |      0.51889      0.04143            0.0076       0.9548
       Factor33  |      0.47746      0.03363            0.0070       0.9618
       Factor34  |      0.44383      0.02168            0.0065       0.9684
       Factor35  |      0.42215      0.02252            0.0062       0.9746
       Factor36  |      0.39963      0.04719            0.0059       0.9804
       Factor37  |      0.35244      0.03025            0.0052       0.9856
       Factor38  |      0.32219      0.00436            0.0047       0.9903
       Factor39  |      0.31783      0.01434            0.0047       0.9950
       Factor40  |      0.30348      0.03578            0.0045       0.9995
       Factor41  |      0.26770      0.02939            0.0039       1.0034
       Factor42  |      0.23832      0.03552            0.0035       1.0069
       Factor43  |      0.20280      0.03176            0.0030       1.0099
       Factor44  |      0.17104      0.00561            0.0025       1.0124
       Factor45  |      0.16543      0.03833            0.0024       1.0148
       Factor46  |      0.12710      0.00686            0.0019       1.0167
       Factor47  |      0.12024      0.01806            0.0018       1.0185
       Factor48  |      0.10219      0.00646            0.0015       1.0200
       Factor49  |      0.09573      0.00497            0.0014       1.0214
       Factor50  |      0.09076      0.03141            0.0013       1.0227
       Factor51  |      0.05934      0.00270            0.0009       1.0236
       Factor52  |      0.05664      0.01279            0.0008       1.0244
       Factor53  |      0.04385      0.01209            0.0006       1.0251
       Factor54  |      0.03177      0.01414            0.0005       1.0255
       Factor55  |      0.01763      0.00608            0.0003       1.0258
       Factor56  |      0.01155      0.00499            0.0002       1.0260
       Factor57  |      0.00656      0.00552            0.0001       1.0261
       Factor58  |      0.00104      0.00068            0.0000       1.0261
       Factor59  |      0.00036      0.00036            0.0000       1.0261
       Factor60  |      0.00000      0.00000            0.0000       1.0261
       Factor61  |      0.00000      0.00000            0.0000       1.0261
       Factor62  |      0.00000      0.00000            0.0000       1.0261
       Factor63  |     -0.00000      0.00000           -0.0000       1.0261
       Factor64  |     -0.00000      0.00852           -0.0000       1.0261
       Factor65  |     -0.00852      0.00054           -0.0001       1.0260
       Factor66  |     -0.00906      0.00985           -0.0001       1.0258
       Factor67  |     -0.01891      0.00552           -0.0003       1.0255
       Factor68  |     -0.02443      0.00730           -0.0004       1.0252
       Factor69  |     -0.03173      0.01339           -0.0005       1.0247
       Factor70  |     -0.04512      0.01176           -0.0007       1.0241
       Factor71  |     -0.05688      0.01430           -0.0008       1.0232
       Factor72  |     -0.07118      0.00272           -0.0010       1.0222
       Factor73  |     -0.07390      0.01376           -0.0011       1.0211
       Factor74  |     -0.08766      0.00729           -0.0013       1.0198
       Factor75  |     -0.09495      0.00617           -0.0014       1.0184
       Factor76  |     -0.10112      0.00883           -0.0015       1.0169
       Factor77  |     -0.10995      0.00374           -0.0016       1.0153
       Factor78  |     -0.11368      0.00554           -0.0017       1.0136
       Factor79  |     -0.11922      0.01783           -0.0018       1.0119
       Factor80  |     -0.13705      0.00397           -0.0020       1.0099
       Factor81  |     -0.14103      0.01744           -0.0021       1.0078
       Factor82  |     -0.15846      0.00640           -0.0023       1.0055
       Factor83  |     -0.16487      0.04187           -0.0024       1.0030
       Factor84  |     -0.20674            .           -0.0030       1.0000
    --------------------------------------------------------------------------
    LR test: independent vs. saturated: chi2(3486)= 1.0e+06 Prob>chi2 = 0.0000

Factor loadings (pattern matrix) and unique variances

    ---------------------------------------------------------------------
        Variable |  Factor1   Factor2   Factor3   Factor4 |   Uniqueness 
    -------------+----------------------------------------+--------------
    partyrbrstmp |   0.1312    0.3713    0.4658   -0.1019 |      0.6175  
       militrank |  -0.5611    0.6482   -0.1294   -0.1470 |      0.2266  
     ldrrotation |  -0.1488    0.1829   -0.1859   -0.0951 |      0.9008  
      milconsult |  -0.4451    0.3498   -0.3554   -0.0787 |      0.5470  
    milmerit_mil |   0.1631   -0.0374   -0.5454   -0.2075 |      0.6315  
    milmeritpers |  -0.1523    0.1520    0.4704    0.2102 |      0.6883  
      milnotrial |  -0.1193    0.3313    0.4059    0.2319 |      0.6575  
      plebiscite |   0.0383    0.2425    0.2533   -0.3014 |      0.7847  
        heirclan |  -0.1439   -0.2839    0.3007    0.2915 |      0.7233  
      officepers |  -0.1709   -0.0355    0.6315   -0.0279 |      0.5700  
     paramilpers |  -0.2230   -0.0903    0.5018    0.0190 |      0.6899  
    ParamilParty |   0.3964    0.1155   -0.1400   -0.0995 |      0.8000  
     ParamilFReb |  -0.0403    0.1448   -0.2088    0.1694 |      0.9051  
    supportparty |   0.7573    0.5178    0.3018    0.0102 |      0.0672  
     partyleader |   0.6194    0.3236    0.2316    0.0702 |      0.4531  
     localorgzns |   0.7639    0.4678    0.2300    0.0151 |      0.1445  
       partymins |   0.7315    0.3895    0.1275    0.0557 |      0.2938  
       excomcivn |   0.7341    0.3090    0.0879    0.0121 |      0.3577  
     multiethnic |   0.5821    0.2738   -0.0194   -0.1406 |      0.5660  
      monoethnic |   0.1026    0.2192    0.3425    0.1792 |      0.7920  
       heirparty |   0.7704    0.0934   -0.2412   -0.0335 |      0.3384  
      heirfamily |  -0.4193   -0.3097    0.4633    0.1582 |      0.4886  
      legcompetn |   0.3808   -0.0533    0.2357   -0.3171 |      0.6960  
    leaderrela~s |  -0.1897   -0.2660    0.4440    0.1430 |      0.6757  
       leaderciv |   0.4726   -0.7022   -0.0151   -0.3513 |      0.1599  
       leadermil |  -0.6193    0.6975   -0.0464   -0.1452 |      0.1068  
     leaderrebel |   0.1973    0.0566    0.0977    0.8046 |      0.3009  
         heirciv |   0.6439   -0.3139   -0.2155   -0.0927 |      0.4319  
          cabciv |   0.3882   -0.4164    0.1699   -0.0243 |      0.6465  
          cabmil |  -0.3688    0.5146   -0.1867    0.0254 |      0.5637  
      partymilit |   0.6102    0.1091   -0.2616    0.2546 |      0.4825  
       ldrPriorD |   0.2572   -0.1463    0.1887   -0.4294 |      0.6924  
        ldrParty |   0.5305   -0.0090   -0.3167   -0.0945 |      0.6093  
          ldrMil |  -0.6130    0.5958   -0.0996   -0.1535 |      0.2358  
        ldrRebel |   0.1777    0.0544    0.0735    0.7388 |      0.4143  
          ldrCiv |   0.0845   -0.0120    0.0544   -0.1175 |      0.9759  
        ldrOther |  -0.0357    0.0136    0.2223   -0.0178 |      0.9488  
        ldrForgn |   0.1421   -0.0442    0.0651   -0.0044 |      0.9736  
        ldrHered |  -0.3607   -0.7197    0.0869    0.0878 |      0.3367  
        SeizCoup |  -0.5676    0.5561   -0.0291   -0.1815 |      0.3348  
       SeizRebel |   0.2489   -0.0993   -0.1419    0.6807 |      0.4447  
       SeizUpris |  -0.0016    0.0215   -0.0879   -0.0679 |      0.9872  
        SeizElec |   0.3377   -0.1465    0.1330   -0.4667 |      0.6290  
        SeizSucc |   0.0260    0.0446    0.1501   -0.0416 |      0.9731  
         SeizFam |  -0.2454   -0.4961    0.0671    0.0264 |      0.6885  
     PartyhNoWin |   0.1405    0.1361    0.0709    0.0880 |      0.9490  
       PartyhWin |   0.0697   -0.0171    0.0873   -0.0471 |      0.9850  
       PartyhReb |   0.3668    0.0935   -0.2328    0.5658 |      0.4825  
    PartyhPrio~m |   0.4326   -0.0096    0.0925   -0.4188 |      0.6288  
    PartyhNopa~y |  -0.7573   -0.5178   -0.3018   -0.0102 |      0.0672  
      PartyhElec |   0.0062   -0.0151    0.1333   -0.0636 |      0.9779  
    MilPartyAlly |  -0.0791    0.2304    0.0335   -0.0742 |      0.9340  
      MilPartyNo |  -0.5958    0.1537   -0.4503   -0.0834 |      0.4117  
    MilPartyPr~r |  -0.0335    0.3641    0.0377   -0.0335 |      0.8638  
      nomilitary |   0.0240   -0.2383    0.0468   -0.1021 |      0.9300  
    milethnic_~e |   0.1596    0.2067   -0.2980   -0.0400 |      0.8414  
    milethnic~ro |  -0.0828   -0.2237    0.1183    0.0173 |      0.9288  
    milethnic~mo |  -0.1235    0.1361    0.2293    0.0866 |      0.9062  
    sectyapp_p~y |   0.4565    0.0372   -0.4456    0.1153 |      0.5784  
    sectyapppers |  -0.1446   -0.1011    0.6191    0.0523 |      0.5828  
    ElecldrPrD~t |   0.0463   -0.0326    0.0293   -0.0290 |      0.9951  
    ElecldrPrDem |   0.0763   -0.1105    0.0651   -0.1887 |      0.9421  
      ElecldrNot |  -0.1208    0.2939   -0.4437    0.2839 |      0.6215  
       Elecldr1C |   0.1372    0.2528    0.2586   -0.1633 |      0.8237  
       Elecldr1F |   0.0600    0.0445    0.0777    0.1467 |      0.9669  
     ElecldrMLeg |   0.2083   -0.0711   -0.0177   -0.1343 |      0.9332  
    ElecldrMExec |   0.1409    0.0721    0.1449   -0.1694 |      0.9253  
    legnoms_in~t |   0.0140    0.0203   -0.1259    0.3447 |      0.8648  
    legnoms_veto |   0.1963    0.1140    0.2659   -0.2439 |      0.8183  
    legnoms_no~o |   0.0707   -0.2819   -0.0663   -0.1220 |      0.8962  
    legnoms_pr~m |   0.0558   -0.0763    0.0293   -0.1690 |      0.9616  
      LdrexHighR |  -0.4964    0.5704   -0.1670   -0.1488 |      0.3782  
       LdrexLowR |  -0.2733    0.2818    0.1459   -0.0365 |      0.8233  
      LdrexRebel |   0.1906    0.0350    0.1032    0.8104 |      0.2951  
      LdrexDemEl |   0.3400   -0.1573    0.1706   -0.4481 |      0.6298  
      LdrexParty |   0.5629   -0.0501   -0.3205   -0.0808 |      0.5714  
      LdrexLoyal |   0.0473    0.0160    0.0455   -0.0610 |      0.9917  
      LdrexReltv |   0.0080    0.0175    0.1703   -0.0030 |      0.9706  
     LdrexRulFam |  -0.3697   -0.7455    0.0987    0.0833 |      0.2908  
      LdrexOther |  -0.0361   -0.0943    0.0391   -0.0803 |      0.9818  
    partye~mpers |   0.1437    0.3811    0.5283    0.0293 |      0.5542  
    partyexcom~n |   0.3953    0.0740   -0.2086    0.0501 |      0.7922  
    partyexcom~e |   0.3279   -0.0055   -0.2100   -0.0242 |      0.8477  
     createparty |  -0.1702    0.3736    0.3917   -0.0166 |      0.6777  
    ---------------------------------------------------------------------

.                 rotate, oblique oblimin  

Factor analysis/correlation                      Number of obs    =      4,171
    Method: principal factors                    Retained factors =          4
    Rotation: oblique oblimin (Kaiser off)       Number of params =        330

    --------------------------------------------------------------------------
         Factor  |     Variance   Proportion    Rotated factors are correlated
    -------------+------------------------------------------------------------
        Factor1  |      9.49969       0.1396
        Factor2  |      8.84691       0.1300
        Factor3  |      6.37132       0.0936
        Factor4  |      4.98279       0.0732
    --------------------------------------------------------------------------
    LR test: independent vs. saturated: chi2(3486)= 1.0e+06 Prob>chi2 = 0.0000

Rotated factor loadings (pattern matrix) and unique variances

    ---------------------------------------------------------------------
        Variable |  Factor1   Factor2   Factor3   Factor4 |   Uniqueness 
    -------------+----------------------------------------+--------------
    partyrbrstmp |   0.5040   -0.1552    0.3940   -0.1145 |      0.6175  
       militrank |   0.0067   -0.8684   -0.0139   -0.1136 |      0.2266  
     ldrrotation |  -0.0365   -0.2673   -0.1555   -0.0727 |      0.9008  
      milconsult |  -0.1928   -0.5971   -0.2466   -0.0410 |      0.5470  
    milmerit_mil |  -0.0746    0.0193   -0.5869   -0.1583 |      0.6315  
    milmeritpers |   0.1306   -0.1223    0.5069    0.1747 |      0.6883  
      milnotrial |   0.2455   -0.2650    0.4302    0.2138 |      0.6575  
      plebiscite |   0.3063   -0.1386    0.2021   -0.3064 |      0.7847  
        heirclan |  -0.2151    0.2154    0.3640    0.2439 |      0.7233  
      officepers |   0.0785    0.0503    0.6569   -0.0887 |      0.5700  
     paramilpers |  -0.0431    0.0448    0.5509   -0.0358 |      0.6899  
    ParamilParty |   0.3128    0.0925   -0.2534   -0.0683 |      0.8000  
     ParamilFReb |  -0.0250   -0.1782   -0.1842    0.1944 |      0.9051  
    supportparty |   0.9630    0.0338    0.0716    0.0396 |      0.0672  
     partyleader |   0.7102    0.1086    0.0526    0.0893 |      0.4531  
     localorgzns |   0.9098    0.0661    0.0026    0.0477 |      0.1445  
       partymins |   0.7966    0.0956   -0.0818    0.0912 |      0.2938  
       excomcivn |   0.7375    0.1565   -0.1211    0.0462 |      0.3577  
     multiethnic |   0.5904    0.0826   -0.1953   -0.1041 |      0.5660  
      monoethnic |   0.3102   -0.0634    0.3101    0.1666 |      0.7920  
       heirparty |   0.5146    0.2960   -0.4448    0.0164 |      0.3384  
      heirfamily |  -0.3510    0.1156    0.5853    0.0870 |      0.4886  
      legcompetn |   0.3463    0.2900    0.1048   -0.3281 |      0.6960  
    leaderrela~s |  -0.1686    0.2000    0.5026    0.0832 |      0.6757  
       leaderciv |  -0.0947    0.8344   -0.1396   -0.3778 |      0.1599  
       leadermil |   0.0268   -0.9260    0.0802   -0.1176 |      0.1068  
     leaderrebel |   0.1134    0.0855    0.1031    0.8048 |      0.3009  
         heirciv |   0.1777    0.5705   -0.3746   -0.0737 |      0.4319  
          cabciv |   0.0584    0.5873    0.0764   -0.0520 |      0.6465  
          cabmil |   0.0129   -0.6616   -0.1017    0.0611 |      0.5637  
      partymilit |   0.3736    0.1953   -0.4001    0.3018 |      0.4825  
       ldrPriorD |   0.1972    0.2908    0.0869   -0.4458 |      0.6924  
        ldrParty |   0.2633    0.2370   -0.4549   -0.0518 |      0.6093  
          ldrMil |  -0.0523   -0.8476    0.0303   -0.1274 |      0.2358  
        ldrRebel |   0.0977    0.0717    0.0798    0.7403 |      0.4143  
          ldrCiv |   0.0830    0.0644    0.0216   -0.1201 |      0.9759  
        ldrOther |   0.0625    0.0092    0.2231   -0.0368 |      0.9488  
        ldrForgn |   0.0923    0.1255    0.0269   -0.0083 |      0.9736  
        ldrHered |  -0.6983    0.4201    0.2157    0.0253 |      0.3367  
        SeizCoup |  -0.0194   -0.7776    0.0860   -0.1624 |      0.3348  
       SeizRebel |  -0.0203    0.1989   -0.1464    0.6934 |      0.4447  
       SeizUpris |  -0.0093   -0.0353   -0.0910   -0.0591 |      0.9872  
        SeizElec |   0.2380    0.3242    0.0086   -0.4758 |      0.6290  
        SeizSucc |   0.1034    0.0035    0.1336   -0.0507 |      0.9731  
         SeizFam |  -0.4727    0.2923    0.1521   -0.0172 |      0.6885  
     PartyhNoWin |   0.1998   -0.0235    0.0329    0.0945 |      0.9490  
       PartyhWin |   0.0725    0.0673    0.0632   -0.0534 |      0.9850  
       PartyhReb |   0.1689    0.0848   -0.2825    0.6017 |      0.4825  
    PartyhPrio~m |   0.3733    0.2548   -0.0576   -0.4133 |      0.6288  
    PartyhNopa~y |  -0.9630   -0.0338   -0.0716   -0.0396 |      0.0672  
      PartyhElec |   0.0476    0.0392    0.1232   -0.0755 |      0.9779  
    MilPartyAlly |   0.1154   -0.2294    0.0384   -0.0653 |      0.9340  
      MilPartyNo |  -0.4567   -0.5327   -0.2911   -0.0542 |      0.4117  
    MilPartyPr~r |   0.2306   -0.3148    0.0282   -0.0155 |      0.8638  
      nomilitary |  -0.1106    0.2187    0.0406   -0.1197 |      0.9300  
    milethnic_~e |   0.1473   -0.1396   -0.3427    0.0028 |      0.8414  
    milethnic~ro |  -0.1642    0.1628    0.1470   -0.0089 |      0.9288  
    milethnic~mo |   0.0718   -0.1380    0.2564    0.0716 |      0.9062  
    sectyapp_p~y |   0.1736    0.1374   -0.5457    0.1692 |      0.5784  
    sectyapppers |   0.0406    0.1177    0.6468   -0.0108 |      0.5828  
    ElecldrPrD~t |   0.0243    0.0572    0.0152   -0.0320 |      0.9951  
    ElecldrPrDem |   0.0251    0.1432    0.0325   -0.1984 |      0.9421  
      ElecldrNot |  -0.0775   -0.3871   -0.3877    0.3353 |      0.6215  
       Elecldr1C |   0.3671   -0.0913    0.1914   -0.1653 |      0.8237  
       Elecldr1F |   0.0800    0.0109    0.0691    0.1445 |      0.9669  
     ElecldrMLeg |   0.1073    0.1675   -0.0802   -0.1305 |      0.9332  
    ElecldrMExec |   0.2137    0.0407    0.0869   -0.1727 |      0.9253  
    legnoms_in~t |  -0.0603   -0.0284   -0.0996    0.3565 |      0.8648  
    legnoms_veto |   0.3295    0.0569    0.1821   -0.2530 |      0.8183  
    legnoms_no~o |  -0.1432    0.2598   -0.0814   -0.1312 |      0.8962  
    legnoms_pr~m |   0.0186    0.0973    0.0034   -0.1742 |      0.9616  
      LdrexHighR |  -0.0119   -0.7754   -0.0646   -0.1150 |      0.3782  
       LdrexLowR |   0.0487   -0.3568    0.1999   -0.0400 |      0.8233  
      LdrexRebel |   0.0958    0.1009    0.1115    0.8085 |      0.2951  
      LdrexDemEl |   0.2432    0.3414    0.0464   -0.4610 |      0.6298  
      LdrexParty |   0.2560    0.2882   -0.4645   -0.0393 |      0.5714  
      LdrexLoyal |   0.0658    0.0199    0.0262   -0.0623 |      0.9917  
      LdrexReltv |   0.0757    0.0204    0.1621   -0.0161 |      0.9706  
     LdrexRulFam |  -0.7167    0.4389    0.2301    0.0179 |      0.2908  
      LdrexOther |  -0.0636    0.0652    0.0450   -0.0904 |      0.9818  
    partye~mpers |   0.5252   -0.1440    0.4612    0.0123 |      0.5542  
    partyexcom~n |   0.2442    0.1156   -0.3062    0.0844 |      0.7922  
    partyexcom~e |   0.1539    0.1443   -0.2924    0.0034 |      0.8477  
     createparty |   0.2618   -0.3329    0.4088   -0.0321 |      0.6777  
    ---------------------------------------------------------------------

Factor rotation matrix

    --------------------------------------------------
                 | Factor1  Factor2  Factor3  Factor4 
    -------------+------------------------------------
         Factor1 |  0.7880   0.6433  -0.3881   0.1156 
         Factor2 |  0.5648  -0.7514  -0.0536   0.0775 
         Factor3 |  0.2421   0.1421   0.9154  -0.0450 
         Factor4 | -0.0384   0.0381   0.0928   0.9892 
    --------------------------------------------------

.                 predict psv1 psv2 psv3 psv4     
(regression scoring assumed)

Scoring coefficients (method = regression; based on oblimin(0) rotated factors)

    ------------------------------------------------------
        Variable |  Factor1   Factor2   Factor3   Factor4 
    -------------+----------------------------------------
    partyrbrstmp |  0.02775  -0.01625   0.03226  -0.00765 
       militrank |  0.02469  -0.03628  -0.05319  -0.02082 
     ldrrotation |  0.00239  -0.01978  -0.02355  -0.00321 
      milconsult | -0.01408  -0.02781  -0.04240  -0.00191 
    milmerit_mil | -0.02072  -0.00590  -0.12378  -0.08247 
    milmeritpers |  0.00936  -0.00946   0.09858   0.00905 
      milnotrial |  0.00881  -0.00630   0.02750   0.00717 
      plebiscite |  0.00718   0.01310   0.02418  -0.00554 
        heirclan |  0.00685   0.01316   0.02584   0.00198 
      officepers |  0.01268   0.00924   0.06787  -0.00846 
     paramilpers | -0.00975   0.00480   0.06085  -0.00059 
    ParamilParty |  0.02037   0.00269  -0.02961  -0.01535 
     ParamilFReb | -0.00533  -0.01069  -0.02459   0.00776 
    supportparty |  0.00000   0.00000   0.00000   0.00000 
     partyleader |  0.00320   0.01356   0.01082  -0.00631 
     localorgzns |  0.05620   0.00608  -0.02965   0.01615 
       partymins |  0.02550   0.03567  -0.01310   0.03131 
       excomcivn |  0.03244  -0.00689  -0.01657  -0.00245 
     multiethnic |  0.14490   0.15952  -0.00488  -0.15145 
      monoethnic |  0.10257   0.12849   0.08421  -0.05265 
       heirparty |  0.04793   0.04918  -0.09325   0.01390 
      heirfamily | -0.00680  -0.00396   0.08719   0.00536 
      legcompetn |  0.02245   0.02879   0.01693  -0.07528 
    leaderrela~s |  0.00127   0.01591   0.05244   0.00457 
       leaderciv |  0.00000   0.00000   0.00000   0.00000 
       leadermil |  0.05377  -0.38386   0.07707   0.07146 
     leaderrebel |  0.00534  -0.05333   0.04399   0.24621 
         heirciv |  0.03279   0.01460  -0.05758  -0.00547 
          cabciv |  0.00939   0.05220   0.00255  -0.02683 
          cabmil |  0.00131  -0.08999  -0.03345  -0.00894 
      partymilit |  0.01965   0.00719  -0.03747   0.04034 
       ldrPriorD |  0.05548   0.11718   0.01594  -0.10349 
        ldrParty |  0.06137   0.14242  -0.07842   0.00599 
          ldrMil |  0.00000   0.00000   0.00000   0.00000 
        ldrRebel |  0.05790   0.10308   0.03590   0.21410 
          ldrCiv |  0.00927   0.04183   0.00397  -0.03145 
        ldrOther |  0.01758   0.06089   0.04644   0.00633 
        ldrForgn |  0.04356   0.10136  -0.01372   0.01353 
        ldrHered | -0.07668   0.14747   0.01904   0.05026 
        SeizCoup | -0.01287  -0.04429  -0.03435  -0.04086 
       SeizRebel |  0.01238   0.00937  -0.05329   0.09711 
       SeizUpris |  0.01056   0.00434  -0.01759  -0.01071 
        SeizElec |  0.00582   0.03013   0.00559  -0.03083 
        SeizSucc |  0.00463   0.01398   0.00872  -0.00076 
         SeizFam | -0.01271   0.01410  -0.00610  -0.01066 
     PartyhNoWin | -0.01936   0.00428  -0.00167  -0.00300 
       PartyhWin | -0.02902   0.00079   0.00633  -0.02590 
       PartyhReb | -0.04192   0.02533  -0.07870   0.07961 
    PartyhPrio~m | -0.02459   0.01998  -0.04597  -0.13282 
    PartyhNopa~y | -0.50645   0.13165  -0.09653  -0.19228 
      PartyhElec | -0.03016  -0.00121   0.01341  -0.00646 
    MilPartyAlly | -0.01121   0.03215   0.00797   0.00965 
      MilPartyNo | -0.02575   0.02006  -0.02821  -0.01889 
    MilPartyPr~r | -0.01628   0.02663   0.00158   0.01598 
      nomilitary | -0.03319   0.04236   0.02920  -0.03793 
    milethnic_~e |  0.00000   0.00000   0.00000   0.00000 
    milethnic~ro | -0.04468   0.05117   0.10147   0.02575 
    milethnic~mo | -0.00891   0.00258   0.09782   0.03061 
    sectyapp_p~y | -0.00303   0.01426  -0.05628   0.04296 
    sectyapppers |  0.00133   0.01980   0.12242   0.00080 
    ElecldrPrD~t | -0.01080  -0.02027   0.00093   0.00363 
    ElecldrPrDem | -0.00667  -0.03528   0.00947  -0.04945 
      ElecldrNot | -0.04823  -0.22122  -0.08528   0.09710 
       Elecldr1C |  0.01628  -0.17948   0.04067  -0.05711 
       Elecldr1F | -0.00037  -0.06241   0.01544   0.04270 
     ElecldrMLeg | -0.02310  -0.05439  -0.01834  -0.02380 
    ElecldrMExec | -0.01384  -0.12508   0.02757  -0.05235 
    legnoms_in~t | -0.00987   0.00082  -0.00868   0.01405 
    legnoms_veto |  0.03517  -0.00622   0.03834  -0.04130 
    legnoms_no~o | -0.02508   0.02202  -0.01614  -0.00058 
    legnoms_pr~m | -0.01220   0.00837   0.00126  -0.00340 
      LdrexHighR |  0.00000   0.00000   0.00000   0.00000 
       LdrexLowR |  0.02329   0.06206   0.03241   0.00866 
      LdrexRebel |  0.00024   0.13834   0.03339   0.24515 
      LdrexDemEl |  0.02238   0.16832   0.00484  -0.03866 
      LdrexParty |  0.03150   0.20296  -0.08836   0.10018 
      LdrexLoyal | -0.00539   0.04462   0.00853  -0.00073 
      LdrexReltv | -0.00745   0.06825   0.04312   0.04533 
     LdrexRulFam | -0.12640   0.11794   0.11954   0.09157 
      LdrexOther | -0.01125   0.06293   0.00494   0.00806 
    partye~mpers |  0.04100  -0.01628   0.14110   0.00348 
    partyexcom~n | -0.01512   0.00130  -0.02302   0.01145 
    partyexcom~e | -0.02542   0.01418  -0.02798  -0.00624 
     createparty | -0.07235   0.03637   0.05332  -0.01241 
    ------------------------------------------------------


.                 factor $allvar1 $allvar2 $allvar3 $allvar4 $allvar5 $allvar6 $allvar
> 7 $allvar8 $allvar9 $allvar10 sv_parties sv_party sv_legindex sv_military sv_mil_cor
> p sv_mil_pers sv_mil_ind, factors(4)            
(obs=4,171)
(collinear variables specified)

Factor analysis/correlation                      Number of obs    =      4,171
    Method: principal factors                    Retained factors =          4
    Rotation: (unrotated)                        Number of params =        358

    --------------------------------------------------------------------------
         Factor  |   Eigenvalue   Difference        Proportion   Cumulative
    -------------+------------------------------------------------------------
        Factor1  |     12.14107      3.70056            0.1628       0.1628
        Factor2  |      8.44051      2.77051            0.1132       0.2760
        Factor3  |      5.67001      0.63250            0.0760       0.3520
        Factor4  |      5.03751      1.81571            0.0675       0.4195
        Factor5  |      3.22180      0.33807            0.0432       0.4627
        Factor6  |      2.88374      0.34532            0.0387       0.5014
        Factor7  |      2.53842      0.33047            0.0340       0.5354
        Factor8  |      2.20795      0.19684            0.0296       0.5651
        Factor9  |      2.01111      0.09184            0.0270       0.5920
       Factor10  |      1.91927      0.16171            0.0257       0.6178
       Factor11  |      1.75756      0.17462            0.0236       0.6413
       Factor12  |      1.58294      0.13756            0.0212       0.6625
       Factor13  |      1.44538      0.01287            0.0194       0.6819
       Factor14  |      1.43251      0.00734            0.0192       0.7011
       Factor15  |      1.42517      0.13463            0.0191       0.7202
       Factor16  |      1.29054      0.03438            0.0173       0.7375
       Factor17  |      1.25616      0.12715            0.0168       0.7544
       Factor18  |      1.12902      0.02043            0.0151       0.7695
       Factor19  |      1.10858      0.03486            0.0149       0.7844
       Factor20  |      1.07372      0.03883            0.0144       0.7988
       Factor21  |      1.03489      0.03750            0.0139       0.8127
       Factor22  |      0.99739      0.03259            0.0134       0.8260
       Factor23  |      0.96480      0.03181            0.0129       0.8390
       Factor24  |      0.93299      0.03070            0.0125       0.8515
       Factor25  |      0.90229      0.03497            0.0121       0.8636
       Factor26  |      0.86732      0.03771            0.0116       0.8752
       Factor27  |      0.82961      0.05417            0.0111       0.8863
       Factor28  |      0.77544      0.04242            0.0104       0.8967
       Factor29  |      0.73302      0.04460            0.0098       0.9066
       Factor30  |      0.68842      0.05370            0.0092       0.9158
       Factor31  |      0.63473      0.00746            0.0085       0.9243
       Factor32  |      0.62726      0.03691            0.0084       0.9327
       Factor33  |      0.59035      0.04219            0.0079       0.9406
       Factor34  |      0.54816      0.02084            0.0074       0.9480
       Factor35  |      0.52732      0.06153            0.0071       0.9551
       Factor36  |      0.46579      0.02448            0.0062       0.9613
       Factor37  |      0.44131      0.01677            0.0059       0.9672
       Factor38  |      0.42455      0.02529            0.0057       0.9729
       Factor39  |      0.39926      0.04051            0.0054       0.9783
       Factor40  |      0.35875      0.02108            0.0048       0.9831
       Factor41  |      0.33767      0.01572            0.0045       0.9876
       Factor42  |      0.32194      0.02531            0.0043       0.9919
       Factor43  |      0.29663      0.02624            0.0040       0.9959
       Factor44  |      0.27039      0.02448            0.0036       0.9995
       Factor45  |      0.24591      0.01063            0.0033       1.0028
       Factor46  |      0.23528      0.04374            0.0032       1.0060
       Factor47  |      0.19154      0.00431            0.0026       1.0085
       Factor48  |      0.18723      0.01254            0.0025       1.0111
       Factor49  |      0.17469      0.03626            0.0023       1.0134
       Factor50  |      0.13843      0.02331            0.0019       1.0153
       Factor51  |      0.11512      0.00760            0.0015       1.0168
       Factor52  |      0.10752      0.00570            0.0014       1.0182
       Factor53  |      0.10182      0.00393            0.0014       1.0196
       Factor54  |      0.09789      0.03317            0.0013       1.0209
       Factor55  |      0.06473      0.00990            0.0009       1.0218
       Factor56  |      0.05483      0.00748            0.0007       1.0225
       Factor57  |      0.04735      0.01566            0.0006       1.0232
       Factor58  |      0.03169      0.00294            0.0004       1.0236
       Factor59  |      0.02875      0.00672            0.0004       1.0240
       Factor60  |      0.02202      0.00840            0.0003       1.0243
       Factor61  |      0.01363      0.00805            0.0002       1.0244
       Factor62  |      0.00557      0.00064            0.0001       1.0245
       Factor63  |      0.00493      0.00451            0.0001       1.0246
       Factor64  |      0.00043      0.00043            0.0000       1.0246
       Factor65  |      0.00000      0.00000            0.0000       1.0246
       Factor66  |      0.00000      0.00000            0.0000       1.0246
       Factor67  |      0.00000      0.00000            0.0000       1.0246
       Factor68  |     -0.00000      0.00000           -0.0000       1.0246
       Factor69  |     -0.00000      0.00491           -0.0000       1.0246
       Factor70  |     -0.00491      0.00914           -0.0001       1.0245
       Factor71  |     -0.01406      0.00145           -0.0002       1.0243
       Factor72  |     -0.01550      0.00759           -0.0002       1.0241
       Factor73  |     -0.02309      0.00173           -0.0003       1.0238
       Factor74  |     -0.02482      0.00314           -0.0003       1.0235
       Factor75  |     -0.02795      0.01833           -0.0004       1.0231
       Factor76  |     -0.04628      0.00845           -0.0006       1.0225
       Factor77  |     -0.05473      0.01072           -0.0007       1.0218
       Factor78  |     -0.06545      0.00379           -0.0009       1.0209
       Factor79  |     -0.06924      0.00959           -0.0009       1.0199
       Factor80  |     -0.07883      0.00519           -0.0011       1.0189
       Factor81  |     -0.08402      0.01165           -0.0011       1.0178
       Factor82  |     -0.09566      0.00773           -0.0013       1.0165
       Factor83  |     -0.10339      0.00446           -0.0014       1.0151
       Factor84  |     -0.10785      0.00353           -0.0014       1.0136
       Factor85  |     -0.11138      0.00845           -0.0015       1.0122
       Factor86  |     -0.11983      0.01241           -0.0016       1.0105
       Factor87  |     -0.13224      0.00783           -0.0018       1.0088
       Factor88  |     -0.14007      0.01356           -0.0019       1.0069
       Factor89  |     -0.15362      0.00813           -0.0021       1.0048
       Factor90  |     -0.16175      0.03735           -0.0022       1.0027
       Factor91  |     -0.19911            .           -0.0027       1.0000
    --------------------------------------------------------------------------
    LR test: independent vs. saturated: chi2(4095)= 1.1e+06 Prob>chi2 = 0.0000

Factor loadings (pattern matrix) and unique variances

    ---------------------------------------------------------------------
        Variable |  Factor1   Factor2   Factor3   Factor4 |   Uniqueness 
    -------------+----------------------------------------+--------------
    partyrbrstmp |   0.1038    0.3749    0.4503   -0.0307 |      0.6450  
       militrank |  -0.6153    0.5990   -0.1056   -0.1438 |      0.2308  
     ldrrotation |  -0.1583    0.1794   -0.1757   -0.1262 |      0.8960  
      milconsult |  -0.4851    0.3035   -0.3468   -0.1016 |      0.5420  
    milmerit_mil |   0.1629   -0.0030   -0.5278   -0.2552 |      0.6298  
    milmeritpers |  -0.1612    0.1165    0.4545    0.2538 |      0.6894  
      milnotrial |  -0.1488    0.3075    0.3832    0.2751 |      0.6608  
      plebiscite |   0.0327    0.2617    0.2727   -0.2647 |      0.7860  
        heirclan |  -0.1197   -0.3044    0.2950    0.2919 |      0.7208  
      officepers |  -0.1512   -0.0502    0.6307    0.0136 |      0.5766  
     paramilpers |  -0.2028   -0.1088    0.5078    0.0405 |      0.6875  
    ParamilParty |   0.3720    0.1378   -0.1588   -0.0573 |      0.8141  
     ParamilFReb |  -0.0563    0.1343   -0.2130    0.1464 |      0.9120  
    supportparty |   0.7035    0.5912    0.2670    0.0800 |      0.0778  
     partyleader |   0.5822    0.3726    0.1956    0.1333 |      0.4662  
     localorgzns |   0.7149    0.5420    0.2001    0.0753 |      0.1495  
       partymins |   0.6793    0.4569    0.0934    0.1189 |      0.3069  
       excomcivn |   0.6982    0.3772    0.0615    0.0597 |      0.3628  
     multiethnic |   0.5573    0.3252   -0.0361   -0.1095 |      0.5704  
      monoethnic |   0.0754    0.2352    0.3257    0.2159 |      0.7863  
       heirparty |   0.7435    0.1605   -0.2646   -0.0050 |      0.3514  
      heirfamily |  -0.3738   -0.3530    0.4709    0.1553 |      0.4898  
      legcompetn |   0.4292    0.0328    0.2774   -0.3564 |      0.6107  
    leaderrela~s |  -0.1535   -0.2887    0.4491    0.1521 |      0.6683  
       leaderciv |   0.5466   -0.6410   -0.0027   -0.3620 |      0.1593  
       leadermil |  -0.6857    0.6427   -0.0271   -0.1280 |      0.0995  
     leaderrebel |   0.1804    0.0422    0.0471    0.7953 |      0.3310  
         heirciv |   0.6591   -0.2528   -0.2273   -0.0888 |      0.4422  
          cabciv |   0.4293   -0.3463    0.1765   -0.0425 |      0.6628  
          cabmil |  -0.4154    0.4504   -0.1913    0.0408 |      0.5863  
      partymilit |   0.5788    0.1360   -0.3033    0.2836 |      0.4741  
       ldrPriorD |   0.2738   -0.0922    0.1907   -0.3999 |      0.7203  
        ldrParty |   0.5258    0.0424   -0.3157   -0.1033 |      0.6114  
          ldrMil |  -0.6736    0.5405   -0.0866   -0.1475 |      0.2250  
        ldrRebel |   0.1630    0.0472    0.0335    0.7241 |      0.4458  
          ldrCiv |   0.0932   -0.0044    0.0574   -0.1092 |      0.9761  
        ldrOther |  -0.0186    0.0166    0.2300   -0.0148 |      0.9462  
        ldrForgn |   0.1461   -0.0417    0.0521    0.0227 |      0.9737  
        ldrHered |  -0.2886   -0.7573    0.1080    0.0490 |      0.3292  
        SeizCoup |  -0.6153    0.5107   -0.0100   -0.1778 |      0.3289  
       SeizRebel |   0.2344   -0.1033   -0.1796    0.6606 |      0.4657  
       SeizUpris |   0.0035    0.0196   -0.0808   -0.0796 |      0.9867  
        SeizElec |   0.3545   -0.0817    0.1397   -0.4434 |      0.6515  
        SeizSucc |   0.0274    0.0419    0.1509   -0.0216 |      0.9742  
         SeizFam |  -0.1910   -0.5169    0.0873   -0.0055 |      0.6887  
     PartyhNoWin |   0.1256    0.1285    0.0541    0.1259 |      0.9489  
       PartyhWin |   0.0762   -0.0105    0.0860   -0.0387 |      0.9852  
       PartyhReb |   0.3362    0.1055   -0.2685    0.5518 |      0.4993  
    PartyhPrio~m |   0.4285    0.0591    0.0947   -0.3784 |      0.6608  
    PartyhNopa~y |  -0.7035   -0.5912   -0.2670   -0.0800 |      0.0778  
      PartyhElec |   0.0214   -0.0175    0.1364   -0.0624 |      0.9767  
    MilPartyAlly |  -0.1031    0.2400    0.0459   -0.0622 |      0.9258  
      MilPartyNo |  -0.6198    0.0887   -0.4337   -0.1144 |      0.4068  
    MilPartyPr~r |  -0.0601    0.3553    0.0461   -0.0131 |      0.8679  
      nomilitary |   0.0422   -0.2287    0.0489   -0.0991 |      0.9337  
    milethnic_~e |   0.1519    0.2023   -0.2958   -0.0480 |      0.8462  
    milethnic~ro |  -0.0677   -0.2273    0.1157    0.0140 |      0.9302  
    milethnic~mo |  -0.1423    0.1418    0.2287    0.1003 |      0.8973  
    sectyapp_p~y |   0.4279    0.0591   -0.4687    0.1232 |      0.5785  
    sectyapppers |  -0.1147   -0.1091    0.6227    0.0708 |      0.5822  
    ElecldrPrD~t |   0.0491   -0.0219    0.0277   -0.0273 |      0.9956  
    ElecldrPrDem |   0.0930   -0.0798    0.0718   -0.1931 |      0.9426  
      ElecldrNot |  -0.1776    0.2389   -0.4781    0.3001 |      0.5927  
       Elecldr1C |   0.1164    0.2658    0.2525   -0.1080 |      0.8404  
       Elecldr1F |   0.0564    0.0437    0.0644    0.1563 |      0.9663  
     ElecldrMLeg |   0.2206   -0.0244   -0.0064   -0.1606 |      0.9249  
    ElecldrMExec |   0.1592    0.1212    0.1751   -0.2058 |      0.8870  
    legnoms_in~t |   0.0011    0.0082   -0.1426    0.3394 |      0.8644  
    legnoms_veto |   0.2137    0.1523    0.2866   -0.2437 |      0.7897  
    legnoms_no~o |   0.1113   -0.2424   -0.0349   -0.1755 |      0.8969  
    legnoms_pr~m |   0.0690   -0.0498    0.0382   -0.1747 |      0.9608  
      LdrexHighR |  -0.5440    0.5306   -0.1426   -0.1534 |      0.3786  
       LdrexLowR |  -0.3143    0.2534    0.1417   -0.0017 |      0.8169  
      LdrexRebel |   0.1792    0.0236    0.0551    0.7934 |      0.3348  
      LdrexDemEl |   0.3546   -0.0941    0.1716   -0.4162 |      0.6627  
      LdrexParty |   0.5574   -0.0057   -0.3299   -0.0753 |      0.5747  
      LdrexLoyal |   0.0565    0.0152    0.0474   -0.0603 |      0.9907  
      LdrexReltv |   0.0211    0.0187    0.1726   -0.0010 |      0.9694  
     LdrexRulFam |  -0.2942   -0.7833    0.1197    0.0439 |      0.2836  
      LdrexOther |  -0.0051   -0.0866    0.0527   -0.1053 |      0.9786  
    partye~mpers |   0.1112    0.3860    0.5055    0.1040 |      0.5723  
    partyexcom~n |   0.3762    0.1106   -0.2191    0.0523 |      0.7955  
    partyexcom~e |   0.3227    0.0306   -0.2138   -0.0382 |      0.8477  
     createparty |  -0.1978    0.3644    0.3863    0.0122 |      0.6787  
      sv_parties |   0.3005    0.3440    0.0648   -0.3301 |      0.6782  
        sv_party |   0.5275    0.5053    0.0594   -0.1247 |      0.4473  
     sv_legindex |   0.4158    0.0147    0.2118   -0.2054 |      0.7398  
     sv_military |  -0.6470    0.6107   -0.0076   -0.0173 |      0.2080  
     sv_mil_corp |  -0.4783    0.3109   -0.1656   -0.0953 |      0.6381  
     sv_mil_pers |  -0.3398    0.4440    0.1766    0.0652 |      0.6519  
    sv_mil_indir |  -0.0818    0.0827   -0.1080   -0.0019 |      0.9748  
    ---------------------------------------------------------------------

.                 rotate, promax(3) 

Factor analysis/correlation                      Number of obs    =      4,171
    Method: principal factors                    Retained factors =          4
    Rotation: oblique promax (Kaiser off)        Number of params =        358

    --------------------------------------------------------------------------
         Factor  |     Variance   Proportion    Rotated factors are correlated
    -------------+------------------------------------------------------------
        Factor1  |     10.42526       0.1398
        Factor2  |     10.36914       0.1390
        Factor3  |      6.32402       0.0848
        Factor4  |      5.09639       0.0683
    --------------------------------------------------------------------------
    LR test: independent vs. saturated: chi2(4095)= 1.1e+06 Prob>chi2 = 0.0000

Rotated factor loadings (pattern matrix) and unique variances

    ---------------------------------------------------------------------
        Variable |  Factor1   Factor2   Factor3   Factor4 |   Uniqueness 
    -------------+----------------------------------------+--------------
    partyrbrstmp |   0.1722    0.4649    0.3809   -0.0540 |      0.6450  
       militrank |   0.8851    0.0245   -0.0335   -0.1494 |      0.2308  
     ldrrotation |   0.2767   -0.0199   -0.1685   -0.1141 |      0.8960  
      milconsult |   0.5998   -0.1765   -0.2672   -0.0876 |      0.5420  
    milmerit_mil |  -0.0068   -0.0559   -0.5873   -0.2010 |      0.6298  
    milmeritpers |   0.1056    0.1231    0.5078    0.2098 |      0.6894  
      milnotrial |   0.2565    0.2577    0.4261    0.2442 |      0.6608  
      plebiscite |   0.1739    0.2715    0.1946   -0.2808 |      0.7860  
        heirclan |  -0.2344   -0.2074    0.3806    0.2514 |      0.7208  
      officepers |  -0.0337    0.0335    0.6485   -0.0523 |      0.5766  
     paramilpers |  -0.0334   -0.0738    0.5496   -0.0188 |      0.6875  
    ParamilParty |  -0.0957    0.2936   -0.2540   -0.0193 |      0.8141  
     ParamilFReb |   0.1566    0.0197   -0.1791    0.1675 |      0.9120  
    supportparty |  -0.0162    0.9670    0.0753    0.1126 |      0.0778  
     partyleader |  -0.1085    0.7102    0.0570    0.1592 |      0.4662  
     localorgzns |  -0.0525    0.9191    0.0109    0.1128 |      0.1495  
       partymins |  -0.0869    0.8074   -0.0713    0.1617 |      0.3069  
       excomcivn |  -0.1516    0.7468   -0.1103    0.1035 |      0.3628  
     multiethnic |  -0.0752    0.5809   -0.1977   -0.0671 |      0.5704  
      monoethnic |   0.0732    0.3259    0.3170    0.1995 |      0.7863  
       heirparty |  -0.2998    0.5216   -0.4306    0.0632 |      0.3514  
      heirfamily |  -0.1256   -0.3680    0.5873    0.0833 |      0.4898  
      legcompetn |  -0.2460    0.3448    0.1137   -0.3595 |      0.6107  
    leaderrela~s |  -0.2088   -0.1861    0.5130    0.0957 |      0.6683  
       leaderciv |  -0.8122   -0.1633   -0.1359   -0.3548 |      0.1593  
       leadermil |   0.9512    0.0347    0.0573   -0.1429 |      0.0995  
     leaderrebel |  -0.1616    0.2134    0.1330    0.8068 |      0.3310  
         heirciv |  -0.5708    0.1605   -0.3604   -0.0418 |      0.4422  
          cabciv |  -0.5616    0.0541    0.0943   -0.0465 |      0.6628  
          cabmil |   0.6373    0.0292   -0.1193    0.0501 |      0.5863  
      partymilit |  -0.2387    0.4086   -0.3831    0.3475 |      0.4741  
       ldrPriorD |  -0.2317    0.1263    0.0657   -0.4076 |      0.7203  
        ldrParty |  -0.2410    0.2744   -0.4400   -0.0464 |      0.6114  
          ldrMil |   0.8729   -0.0517    0.0012   -0.1597 |      0.2250  
        ldrRebel |  -0.1381    0.1977    0.1117    0.7357 |      0.4458  
          ldrCiv |  -0.0591    0.0632    0.0175   -0.1102 |      0.9761  
        ldrOther |  -0.0056    0.0603    0.2228   -0.0359 |      0.9462  
        ldrForgn |  -0.1334    0.0763    0.0251    0.0247 |      0.9737  
        ldrHered |  -0.4378   -0.7219    0.2292    0.0007 |      0.3292  
        SeizCoup |   0.8054   -0.0192    0.0596   -0.1948 |      0.3289  
       SeizRebel |  -0.2660    0.0688   -0.1096    0.6900 |      0.4657  
       SeizUpris |   0.0321   -0.0098   -0.0932   -0.0721 |      0.9867  
        SeizElec |  -0.2625    0.1689   -0.0092   -0.4423 |      0.6515  
        SeizSucc |  -0.0028    0.0872    0.1334   -0.0324 |      0.9742  
         SeizFam |  -0.3007   -0.4881    0.1619   -0.0396 |      0.6887  
     PartyhNoWin |   0.0037    0.1994    0.0362    0.1326 |      0.9489  
       PartyhWin |  -0.0640    0.0603    0.0609   -0.0429 |      0.9852  
       PartyhReb |  -0.1419    0.2598   -0.2504    0.6004 |      0.4993  
    PartyhPrio~m |  -0.1975    0.3145   -0.0682   -0.3645 |      0.6608  
    PartyhNopa~y |   0.0162   -0.9670   -0.0753   -0.1126 |      0.0778  
      PartyhElec |  -0.0400    0.0320    0.1182   -0.0744 |      0.9767  
    MilPartyAlly |   0.2534    0.1235    0.0398   -0.0647 |      0.9258  
      MilPartyNo |   0.5273   -0.4475   -0.3087   -0.1066 |      0.4068  
    MilPartyPr~r |   0.3130    0.2412    0.0304   -0.0094 |      0.8679  
      nomilitary |  -0.2042   -0.1398    0.0381   -0.1089 |      0.9337  
    milethnic_~e |   0.1105    0.1674   -0.3414   -0.0075 |      0.8462  
    milethnic~ro |  -0.1546   -0.1828    0.1450   -0.0069 |      0.9302  
    milethnic~mo |   0.1596    0.0841    0.2585    0.0774 |      0.8973  
    sectyapp_p~y |  -0.1672    0.2005   -0.5307    0.1904 |      0.5785  
    sectyapppers |  -0.1074    0.0140    0.6462    0.0060 |      0.5822  
    ElecldrPrD~t |  -0.0491    0.0200    0.0131   -0.0280 |      0.9956  
    ElecldrPrDem |  -0.1125    0.0042    0.0232   -0.1982 |      0.9426  
      ElecldrNot |   0.3368   -0.0374   -0.3912    0.3427 |      0.5927  
       Elecldr1C |   0.1129    0.3331    0.1821   -0.1167 |      0.8404  
       Elecldr1F |  -0.0244    0.0965    0.0723    0.1559 |      0.9663  
     ElecldrMLeg |  -0.1406    0.1084   -0.0790   -0.1501 |      0.9249  
    ElecldrMExec |  -0.0080    0.2240    0.0922   -0.2105 |      0.8870  
    legnoms_in~t |  -0.0069   -0.0072   -0.0832    0.3545 |      0.8644  
    legnoms_veto |  -0.0292    0.3087    0.1796   -0.2546 |      0.7897  
    legnoms_no~o |  -0.2393   -0.1338   -0.0696   -0.1750 |      0.8969  
    legnoms_pr~m |  -0.0710    0.0040   -0.0032   -0.1771 |      0.9608  
      LdrexHighR |   0.7926    0.0077   -0.0816   -0.1541 |      0.3786  
       LdrexLowR |   0.3767    0.0293    0.1875   -0.0233 |      0.8169  
      LdrexRebel |  -0.1764    0.2006    0.1420    0.8036 |      0.3348  
      LdrexDemEl |  -0.2794    0.1699    0.0269   -0.4181 |      0.6627  
      LdrexParty |  -0.2993    0.2564   -0.4527   -0.0167 |      0.5747  
      LdrexLoyal |  -0.0241    0.0555    0.0225   -0.0614 |      0.9907  
      LdrexReltv |  -0.0221    0.0729    0.1607   -0.0147 |      0.9694  
     LdrexRulFam |  -0.4560   -0.7424    0.2428   -0.0066 |      0.2836  
      LdrexOther |  -0.0625   -0.0620    0.0410   -0.1136 |      0.9786  
    partye~mpers |   0.1558    0.5019    0.4539    0.0774 |      0.5723  
    partyexcom~n |  -0.1220    0.2674   -0.2934    0.0957 |      0.7955  
    partyexcom~e |  -0.1440    0.1684   -0.2856   -0.0012 |      0.8477  
     createparty |   0.3567    0.2523    0.3928   -0.0214 |      0.6787  
      sv_parties |   0.1069    0.4438   -0.0815   -0.3112 |      0.6782  
        sv_party |   0.0739    0.7220   -0.1142   -0.0869 |      0.4473  
     sv_legindex |  -0.2573    0.3158    0.0792   -0.2029 |      0.7398  
     sv_military |   0.8886    0.0479    0.0880   -0.0322 |      0.2080  
     sv_mil_corp |   0.5758   -0.1184   -0.0931   -0.0969 |      0.6381  
     sv_mil_pers |   0.5320    0.1708    0.2241    0.0455 |      0.6519  
    sv_mil_indir |   0.1314   -0.0180   -0.0927    0.0059 |      0.9748  
    ---------------------------------------------------------------------

Factor rotation matrix

    --------------------------------------------------
                 | Factor1  Factor2  Factor3  Factor4 
    -------------+------------------------------------
         Factor1 | -0.7397   0.7479  -0.3160  -0.0703 
         Factor2 |  0.6620   0.6244  -0.0727   0.0316 
         Factor3 | -0.1202   0.2217   0.9404  -0.1794 
         Factor4 |  0.0080  -0.0392   0.1025   0.9808 
    --------------------------------------------------

.                 predict sv1 sv2 sv3 sv4 
(regression scoring assumed)

Scoring coefficients (method = regression; based on promax(3) rotated factors)

    ------------------------------------------------------
        Variable |  Factor1   Factor2   Factor3   Factor4 
    -------------+----------------------------------------
    partyrbrstmp |  0.01337   0.02323   0.03130  -0.00776 
       militrank |  0.04073   0.00752  -0.06331  -0.00980 
     ldrrotation |  0.01629   0.00508  -0.02241  -0.00234 
      milconsult |  0.02221  -0.00733  -0.03939  -0.00152 
    milmerit_mil | -0.00118  -0.01020  -0.11568  -0.07584 
    milmeritpers |  0.00308   0.01161   0.10180   0.00466 
      milnotrial |  0.00720   0.00546   0.02679   0.01034 
      plebiscite | -0.01052   0.00610   0.02330  -0.01060 
        heirclan | -0.00857   0.00343   0.02400  -0.00048 
      officepers | -0.00877   0.01376   0.07085  -0.01253 
     paramilpers | -0.00152  -0.01113   0.06019  -0.00629 
    ParamilParty | -0.00177   0.01558  -0.02988  -0.01368 
     ParamilFReb |  0.00867  -0.00485  -0.02451   0.00801 
    supportparty |  0.00000   0.00000   0.00000   0.00000 
     partyleader | -0.01086  -0.00021   0.00998  -0.00513 
     localorgzns | -0.00936   0.06020  -0.02367   0.01369 
       partymins | -0.02869   0.01946  -0.01615   0.02573 
       excomcivn |  0.00755   0.02788  -0.01787  -0.00118 
     multiethnic | -0.14724   0.15011   0.01037  -0.18280 
      monoethnic | -0.11383   0.10677   0.09300  -0.08381 
       heirparty | -0.04535   0.03821  -0.08944   0.01641 
      heirfamily |  0.00158  -0.00824   0.08831   0.00519 
      legcompetn | -0.04022   0.05108   0.03376  -0.11319 
    leaderrela~s | -0.01221   0.00053   0.05109   0.00097 
       leaderciv |  0.00000   0.00000   0.00000   0.00000 
       leadermil |  0.32328   0.05988   0.07777   0.08986 
     leaderrebel |  0.05646  -0.01265   0.03144   0.23857 
         heirciv | -0.01511   0.02923  -0.05463  -0.00718 
          cabciv | -0.04294   0.01487   0.00554  -0.03381 
          cabmil |  0.07387  -0.00072  -0.03520   0.00645 
      partymilit | -0.00477   0.00968  -0.03968   0.04628 
       ldrPriorD | -0.09989   0.04751   0.01790  -0.10304 
        ldrParty | -0.12363   0.05695  -0.07017  -0.00585 
          ldrMil |  0.00000   0.00000   0.00000   0.00000 
        ldrRebel | -0.08104   0.04582   0.03165   0.18836 
          ldrCiv | -0.03743   0.00877   0.00564  -0.03221 
        ldrOther | -0.05163   0.01604   0.04632  -0.00486 
        ldrForgn | -0.08719   0.03582  -0.01252   0.01174 
        ldrHered | -0.13139  -0.06460   0.02332   0.03765 
        SeizCoup |  0.03526   0.00039  -0.02913  -0.03760 
       SeizRebel | -0.00099   0.00756  -0.05661   0.09607 
       SeizUpris | -0.00670   0.01205  -0.01381  -0.00909 
        SeizElec | -0.02551   0.00914   0.00727  -0.03473 
        SeizSucc | -0.01192   0.00178   0.00861  -0.00015 
         SeizFam | -0.01227  -0.00938  -0.00452  -0.01295 
     PartyhNoWin | -0.00308  -0.01819  -0.00204   0.00145 
       PartyhWin | -0.00005  -0.02639   0.00646  -0.02338 
       PartyhReb | -0.01859  -0.03504  -0.07834   0.07494 
    PartyhPrio~m | -0.02078  -0.01114  -0.03857  -0.12262 
    PartyhNopa~y | -0.10341  -0.44430  -0.10839  -0.17012 
      PartyhElec |  0.00338  -0.03170   0.01035  -0.00278 
    MilPartyAlly | -0.02199  -0.01617   0.00262   0.00872 
      MilPartyNo | -0.01658  -0.02521  -0.03067  -0.01658 
    MilPartyPr~r | -0.02116  -0.01965  -0.00139   0.01460 
      nomilitary | -0.03428  -0.02860   0.02965  -0.03531 
    milethnic_~e |  0.00000   0.00000   0.00000   0.00000 
    milethnic~ro | -0.03697  -0.04226   0.09888   0.02286 
    milethnic~mo |  0.00514  -0.00677   0.09566   0.02878 
    sectyapp_p~y | -0.01136  -0.00615  -0.05705   0.04343 
    sectyapppers | -0.01732   0.00405   0.12223  -0.00714 
    ElecldrPrD~t |  0.01470  -0.00630   0.00242   0.00615 
    ElecldrPrDem |  0.02266   0.00692   0.01591  -0.04784 
      ElecldrNot |  0.17206  -0.03675  -0.08287   0.12715 
       Elecldr1C |  0.13024   0.03158   0.05374  -0.03762 
       Elecldr1F |  0.04730   0.00547   0.01752   0.04439 
     ElecldrMLeg |  0.03528  -0.00068  -0.00699  -0.02704 
    ElecldrMExec |  0.08795   0.01705   0.04341  -0.05685 
    legnoms_in~t | -0.00174  -0.00559  -0.00916   0.01236 
    legnoms_veto |  0.00278   0.03391   0.03994  -0.04095 
    legnoms_no~o | -0.01984  -0.01721  -0.01326  -0.00336 
    legnoms_pr~m | -0.00667  -0.01106   0.00038  -0.00196 
      LdrexHighR |  0.00000   0.00000   0.00000   0.00000 
       LdrexLowR | -0.04550   0.01113   0.02586   0.00687 
      LdrexRebel | -0.11174  -0.00356   0.03095   0.22379 
      LdrexDemEl | -0.14064   0.01657   0.00732  -0.04086 
      LdrexParty | -0.16929   0.01614  -0.08756   0.09624 
      LdrexLoyal | -0.03772  -0.00834   0.00820   0.00001 
      LdrexReltv | -0.05650  -0.01104   0.04224   0.03970 
     LdrexRulFam | -0.09465  -0.11660   0.11946   0.09186 
      LdrexOther | -0.05357  -0.00733   0.00576   0.00029 
    partye~mpers |  0.01226   0.03313   0.13937   0.00154 
    partyexcom~n | -0.00217  -0.01448  -0.02262   0.01279 
    partyexcom~e | -0.01266  -0.02271  -0.02726  -0.00673 
     createparty | -0.02766  -0.06479   0.04965  -0.01329 
      sv_parties |  0.00757   0.01504  -0.01445  -0.06488 
        sv_party |  0.00785   0.09583  -0.00472  -0.03460 
     sv_legindex | -0.00560   0.00563   0.00171  -0.02037 
     sv_military |  0.18787  -0.08175  -0.07009   0.02712 
     sv_mil_corp | -0.00562   0.01700   0.01923  -0.01536 
     sv_mil_pers | -0.03327   0.06997   0.10548   0.00934 
    sv_mil_indir | -0.02362   0.01523   0.01074   0.00617 
    ------------------------------------------------------


.                 pwcorr pr1 pr2 pr3 pr4 psv1 psv2 psv3 psv4

             |      pr1      pr2      pr3      pr4     psv1     psv2     psv3
-------------+---------------------------------------------------------------
         pr1 |   1.0000 
         pr2 |  -0.1649   1.0000 
         pr3 |  -0.1033   0.0773   1.0000 
         pr4 |  -0.0596   0.0733  -0.0525   1.0000 
        psv1 |   0.9983  -0.1442  -0.1122  -0.0105   1.0000 
        psv2 |   0.1481  -0.9989  -0.0709  -0.0344   0.1293   1.0000 
        psv3 |  -0.1136   0.0704   0.9989  -0.0216  -0.1211  -0.0621   1.0000 
        psv4 |   0.0438  -0.0159  -0.0154   0.9895   0.0907   0.0533   0.0154 

             |     psv4
-------------+---------
        psv4 |   1.0000 

.                 pwcorr pr1 sv2

             |      pr1      sv2
-------------+------------------
         pr1 |   1.0000 
         sv2 |   0.9927   1.0000 

.                 pwcorr pr2 sv1

             |      pr2      sv1
-------------+------------------
         pr2 |   1.0000 
         sv1 |   0.9959   1.0000 

.                 drop psv* sv2 sv3 sv4   

.                 * HT comparison *
.                 factor $allvar1 $allvar2 $allvar3 $allvar4 $allvar5 $allvar6 $allvar
> 7 $allvar8 $allvar9 $allvar10 if ht_parties~=., factors(4)
(obs=2,085)
(collinear variables specified)

Factor analysis/correlation                      Number of obs    =      2,085
    Method: principal factors                    Retained factors =          4
    Rotation: (unrotated)                        Number of params =        330

    --------------------------------------------------------------------------
         Factor  |   Eigenvalue   Difference        Proportion   Cumulative
    -------------+------------------------------------------------------------
        Factor1  |      9.87519      2.35065            0.1397       0.1397
        Factor2  |      7.52454      2.17668            0.1065       0.2462
        Factor3  |      5.34786      0.09066            0.0757       0.3219
        Factor4  |      5.25720      2.17526            0.0744       0.3963
        Factor5  |      3.08194      0.21735            0.0436       0.4399
        Factor6  |      2.86459      0.27891            0.0405       0.4805
        Factor7  |      2.58568      0.12220            0.0366       0.5171
        Factor8  |      2.46348      0.29596            0.0349       0.5519
        Factor9  |      2.16752      0.05306            0.0307       0.5826
       Factor10  |      2.11445      0.29068            0.0299       0.6125
       Factor11  |      1.82378      0.03086            0.0258       0.6383
       Factor12  |      1.79291      0.16072            0.0254       0.6637
       Factor13  |      1.63219      0.05113            0.0231       0.6868
       Factor14  |      1.58106      0.08711            0.0224       0.7092
       Factor15  |      1.49395      0.18384            0.0211       0.7303
       Factor16  |      1.31012      0.05765            0.0185       0.7488
       Factor17  |      1.25247      0.06024            0.0177       0.7666
       Factor18  |      1.19222      0.04849            0.0169       0.7834
       Factor19  |      1.14373      0.07117            0.0162       0.7996
       Factor20  |      1.07257      0.01533            0.0152       0.8148
       Factor21  |      1.05724      0.06419            0.0150       0.8298
       Factor22  |      0.99305      0.05118            0.0141       0.8438
       Factor23  |      0.94186      0.06473            0.0133       0.8572
       Factor24  |      0.87713      0.02461            0.0124       0.8696
       Factor25  |      0.85252      0.03891            0.0121       0.8816
       Factor26  |      0.81361      0.02316            0.0115       0.8931
       Factor27  |      0.79045      0.08020            0.0112       0.9043
       Factor28  |      0.71025      0.01550            0.0101       0.9144
       Factor29  |      0.69475      0.03842            0.0098       0.9242
       Factor30  |      0.65633      0.02440            0.0093       0.9335
       Factor31  |      0.63193      0.04870            0.0089       0.9424
       Factor32  |      0.58323      0.05432            0.0083       0.9507
       Factor33  |      0.52891      0.04110            0.0075       0.9582
       Factor34  |      0.48781      0.05656            0.0069       0.9651
       Factor35  |      0.43125      0.04402            0.0061       0.9712
       Factor36  |      0.38723      0.01661            0.0055       0.9767
       Factor37  |      0.37062      0.03811            0.0052       0.9819
       Factor38  |      0.33251      0.02715            0.0047       0.9866
       Factor39  |      0.30536      0.00659            0.0043       0.9909
       Factor40  |      0.29878      0.06074            0.0042       0.9952
       Factor41  |      0.23803      0.01314            0.0034       0.9985
       Factor42  |      0.22489      0.02521            0.0032       1.0017
       Factor43  |      0.19968      0.00233            0.0028       1.0045
       Factor44  |      0.19735      0.03032            0.0028       1.0073
       Factor45  |      0.16703      0.02572            0.0024       1.0097
       Factor46  |      0.14131      0.01295            0.0020       1.0117
       Factor47  |      0.12836      0.02834            0.0018       1.0135
       Factor48  |      0.10002      0.01114            0.0014       1.0149
       Factor49  |      0.08889      0.00342            0.0013       1.0162
       Factor50  |      0.08547      0.01139            0.0012       1.0174
       Factor51  |      0.07408      0.00709            0.0010       1.0184
       Factor52  |      0.06698      0.01378            0.0009       1.0194
       Factor53  |      0.05320      0.00460            0.0008       1.0201
       Factor54  |      0.04860      0.02652            0.0007       1.0208
       Factor55  |      0.02208      0.00945            0.0003       1.0211
       Factor56  |      0.01264      0.00121            0.0002       1.0213
       Factor57  |      0.01143      0.00804            0.0002       1.0215
       Factor58  |      0.00339      0.00339            0.0000       1.0215
       Factor59  |      0.00000      0.00000            0.0000       1.0215
       Factor60  |      0.00000      0.00000            0.0000       1.0215
       Factor61  |      0.00000      0.00000            0.0000       1.0215
       Factor62  |      0.00000      0.00000            0.0000       1.0215
       Factor63  |     -0.00000      0.00000           -0.0000       1.0215
       Factor64  |     -0.00000      0.00000           -0.0000       1.0215
       Factor65  |     -0.00000      0.00227           -0.0000       1.0215
       Factor66  |     -0.00227      0.00624           -0.0000       1.0215
       Factor67  |     -0.00851      0.00596           -0.0001       1.0214
       Factor68  |     -0.01448      0.00970           -0.0002       1.0212
       Factor69  |     -0.02417      0.00688           -0.0003       1.0208
       Factor70  |     -0.03105      0.01424           -0.0004       1.0204
       Factor71  |     -0.04529      0.00682           -0.0006       1.0198
       Factor72  |     -0.05211      0.00900           -0.0007       1.0190
       Factor73  |     -0.06111      0.00772           -0.0009       1.0182
       Factor74  |     -0.06883      0.01510           -0.0010       1.0172
       Factor75  |     -0.08393      0.00780           -0.0012       1.0160
       Factor76  |     -0.09172      0.00785           -0.0013       1.0147
       Factor77  |     -0.09957      0.00579           -0.0014       1.0133
       Factor78  |     -0.10536      0.00516           -0.0015       1.0118
       Factor79  |     -0.11052      0.00929           -0.0016       1.0102
       Factor80  |     -0.11981      0.00886           -0.0017       1.0085
       Factor81  |     -0.12867      0.01377           -0.0018       1.0067
       Factor82  |     -0.14244      0.00919           -0.0020       1.0047
       Factor83  |     -0.15162      0.02878           -0.0021       1.0026
       Factor84  |     -0.18040            .           -0.0026       1.0000
    --------------------------------------------------------------------------
    LR test: independent vs. saturated: chi2(3486)= 6.4e+05 Prob>chi2 = 0.0000

Factor loadings (pattern matrix) and unique variances

    ---------------------------------------------------------------------
        Variable |  Factor1   Factor2   Factor3   Factor4 |   Uniqueness 
    -------------+----------------------------------------+--------------
    partyrbrstmp |  -0.1781    0.4725   -0.2103    0.2787 |      0.6231  
       militrank |  -0.5094    0.5513   -0.0604   -0.4982 |      0.1847  
     ldrrotation |  -0.1684    0.0875   -0.0453   -0.4533 |      0.7565  
      milconsult |  -0.2141    0.2125    0.0136   -0.4508 |      0.7056  
    milmerit_mil |   0.3491   -0.2042   -0.3119   -0.4421 |      0.5438  
    milmeritpers |  -0.3222    0.2141    0.2988    0.3946 |      0.6053  
      milnotrial |  -0.2771    0.3458    0.3031    0.3449 |      0.5928  
      plebiscite |  -0.1879    0.2770   -0.3514    0.0851 |      0.7573  
        heirclan |  -0.1843   -0.1278    0.3352    0.2060 |      0.7949  
      officepers |  -0.5146    0.0456   -0.1079    0.5075 |      0.4639  
     paramilpers |  -0.4228    0.0631   -0.0331    0.3938 |      0.6611  
    ParamilParty |   0.3900    0.1316   -0.0608   -0.0789 |      0.8206  
     ParamilFReb |   0.0084   -0.0058    0.2030   -0.2635 |      0.8892  
    supportparty |   0.5074    0.7447   -0.0155    0.2790 |      0.1099  
     partyleader |   0.3986    0.4014    0.0226    0.3177 |      0.5786  
     localorgzns |   0.5325    0.6659   -0.0415    0.2135 |      0.2258  
       partymins |   0.5530    0.5368    0.0457    0.1324 |      0.3864  
       excomcivn |   0.5785    0.3925   -0.0623    0.1075 |      0.4959  
     multiethnic |   0.3872    0.3411   -0.2821   -0.0638 |      0.6501  
      monoethnic |  -0.0337    0.2020    0.2970    0.2865 |      0.7877  
       heirparty |   0.7645    0.1508   -0.1021   -0.0787 |      0.3762  
      heirfamily |  -0.5740   -0.2237    0.1532    0.3425 |      0.4796  
      legcompetn |  -0.1385   -0.0777   -0.3631   -0.0558 |      0.8398  
    leaderrela~s |  -0.3372   -0.1075    0.1530    0.3629 |      0.7196  
       leaderciv |   0.4520   -0.6061   -0.4504    0.2469 |      0.1645  
       leadermil |  -0.5941    0.6089   -0.0475   -0.4096 |      0.1062  
     leaderrebel |   0.1662    0.0555    0.7828    0.2170 |      0.3094  
         heirciv |   0.7391   -0.1677   -0.1576    0.0816 |      0.3942  
          cabciv |   0.2017   -0.3849   -0.0001    0.1760 |      0.7802  
          cabmil |  -0.2224    0.4237    0.0510   -0.2478 |      0.7071  
      partymilit |   0.6759    0.0649    0.2590   -0.0661 |      0.4674  
       ldrPriorD |   0.1242   -0.0971   -0.4250    0.3504 |      0.6717  
        ldrParty |   0.5518   -0.0451   -0.1517   -0.2260 |      0.6194  
          ldrMil |  -0.5602    0.4506   -0.0521   -0.3657 |      0.3466  
        ldrRebel |   0.1494    0.0374    0.7032    0.2167 |      0.4348  
          ldrCiv |   0.0601    0.0209   -0.1749    0.0947 |      0.9564  
        ldrOther |  -0.1587    0.0392    0.0389    0.1871 |      0.9367  
        ldrForgn |   0.0617    0.0951    0.0237    0.0906 |      0.9784  
        ldrHered |  -0.3796   -0.7000    0.0847    0.0173 |      0.3585  
        SeizCoup |  -0.5174    0.4710   -0.0719   -0.2164 |      0.4585  
       SeizRebel |   0.4153   -0.0065    0.6656   -0.0815 |      0.3779  
       SeizUpris |   0.0059   -0.0085   -0.0748   -0.1125 |      0.9816  
        SeizElec |   0.2450   -0.1088   -0.5183    0.2934 |      0.5734  
        SeizSucc |  -0.1312    0.0852   -0.0131    0.0300 |      0.9744  
         SeizFam |  -0.2873   -0.5771    0.0582   -0.0036 |      0.5811  
     PartyhNoWin |   0.0136    0.1442    0.0454    0.1111 |      0.9646  
       PartyhWin |  -0.0434    0.0323   -0.0402    0.0156 |      0.9952  
       PartyhReb |   0.4354    0.0331    0.5208   -0.1694 |      0.5094  
    PartyhPrio~m |   0.3733    0.0300   -0.4378    0.2489 |      0.6061  
    PartyhNopa~y |  -0.5074   -0.7447    0.0155   -0.2790 |      0.1099  
      PartyhElec |  -0.0567   -0.0239   -0.0368    0.1040 |      0.9841  
    MilPartyAlly |  -0.1106    0.1655   -0.0241   -0.1316 |      0.9424  
      MilPartyNo |  -0.2755   -0.1063    0.0220   -0.4780 |      0.6838  
    MilPartyPr~r |  -0.1444    0.3882   -0.0629   -0.3219 |      0.7209  
      nomilitary |   0.0156   -0.0823   -0.1140    0.0665 |      0.9756  
    milethnic_~e |   0.2501   -0.0250    0.0051   -0.3530 |      0.8122  
    milethnic~ro |  -0.1015   -0.0061   -0.1152    0.2242 |      0.9261  
    milethnic~mo |  -0.2132    0.0677    0.1819    0.1656 |      0.8895  
    sectyapp_p~y |   0.5504   -0.0475    0.1825   -0.3766 |      0.5196  
    sectyapppers |  -0.4586    0.0678   -0.0115    0.5016 |      0.5333  
    ElecldrPrD~t |  -0.0167   -0.0586   -0.0440    0.0839 |      0.9873  
    ElecldrPrDem |  -0.0183   -0.1243   -0.0786    0.0786 |      0.9719  
      ElecldrNot |   0.2865    0.0463    0.3505   -0.3426 |      0.6756  
       Elecldr1C |  -0.0962    0.3585   -0.2019    0.1606 |      0.7957  
       Elecldr1F |   0.0008    0.0757    0.1736    0.1066 |      0.9528  
     ElecldrMLeg |   0.1744   -0.0323   -0.1467    0.0397 |      0.9454  
    ElecldrMExec |  -0.0549    0.0324   -0.1413    0.0455 |      0.9739  
    legnoms_in~t |   0.1623   -0.1103    0.4349   -0.0917 |      0.7640  
    legnoms_veto |  -0.0910    0.1977   -0.3145    0.0485 |      0.8514  
    legnoms_no~o |  -0.0395   -0.3520   -0.0555   -0.1362 |      0.8529  
    legnoms_pr~m |   0.0243   -0.0304   -0.0418    0.0504 |      0.9942  
      LdrexHighR |  -0.4384    0.4641   -0.0884   -0.4624 |      0.3708  
       LdrexLowR |  -0.3296    0.2951    0.0050    0.0312 |      0.8033  
      LdrexRebel |   0.1607    0.0511    0.7775    0.2515 |      0.3038  
      LdrexDemEl |   0.2070   -0.0553   -0.4177    0.3697 |      0.6429  
      LdrexParty |   0.5893   -0.1305   -0.1675   -0.1139 |      0.5946  
      LdrexLoyal |   0.0330   -0.0185   -0.0843   -0.0187 |      0.9911  
      LdrexReltv |  -0.0838    0.0425    0.0305    0.1150 |      0.9770  
     LdrexRulFam |  -0.3796   -0.7000    0.0847    0.0173 |      0.3585  
      LdrexOther |  -0.1136   -0.1867   -0.1018    0.0275 |      0.9411  
    partye~mpers |  -0.1886    0.5023   -0.0938    0.3542 |      0.5779  
    partyexcom~n |   0.4439    0.0035    0.1614   -0.1080 |      0.7652  
    partyexcom~e |   0.3274   -0.0198   -0.0571   -0.1735 |      0.8591  
     createparty |  -0.4205    0.3934    0.0260    0.1511 |      0.6449  
    ---------------------------------------------------------------------

.                 rotate, promax(3) 

Factor analysis/correlation                      Number of obs    =      2,085
    Method: principal factors                    Retained factors =          4
    Rotation: oblique promax (Kaiser off)        Number of params =        330

    --------------------------------------------------------------------------
         Factor  |     Variance   Proportion    Rotated factors are correlated
    -------------+------------------------------------------------------------
        Factor1  |      8.06461       0.1141
        Factor2  |      7.72863       0.1094
        Factor3  |      7.58928       0.1074
        Factor4  |      5.42624       0.0768
    --------------------------------------------------------------------------
    LR test: independent vs. saturated: chi2(3486)= 6.4e+05 Prob>chi2 = 0.0000

Rotated factor loadings (pattern matrix) and unique variances

    ---------------------------------------------------------------------
        Variable |  Factor1   Factor2   Factor3   Factor4 |   Uniqueness 
    -------------+----------------------------------------+--------------
    partyrbrstmp |   0.4006    0.1779    0.3899   -0.1784 |      0.6231  
       militrank |   0.0564    0.8998   -0.0408   -0.1563 |      0.1847  
     ldrrotation |  -0.1430    0.4235   -0.2656   -0.1129 |      0.7565  
      milconsult |  -0.0675    0.5251   -0.2170   -0.0548 |      0.7056  
    milmerit_mil |  -0.0996   -0.0354   -0.6082   -0.3205 |      0.5438  
    milmeritpers |   0.1080    0.0579    0.5544    0.3064 |      0.6053  
      milnotrial |   0.2229    0.1484    0.5061    0.3170 |      0.5928  
      plebiscite |   0.1900    0.1767    0.2024   -0.3494 |      0.7573  
        heirclan |  -0.1634   -0.0932    0.2728    0.3237 |      0.7949  
      officepers |  -0.0560   -0.0570    0.7172   -0.1157 |      0.4639  
     paramilpers |  -0.0368   -0.0117    0.5762   -0.0415 |      0.6611  
    ParamilParty |   0.2823   -0.0642   -0.2747   -0.0117 |      0.8206  
     ParamilFReb |  -0.0969    0.1787   -0.2139    0.1721 |      0.8892  
    supportparty |   0.9454    0.0252    0.0405    0.1163 |      0.1099  
     partyleader |   0.6186   -0.1526    0.0886    0.1282 |      0.5786  
     localorgzns |   0.8759    0.0050   -0.0402    0.0830 |      0.2258  
       partymins |   0.7486   -0.0243   -0.1332    0.1569 |      0.3864  
       excomcivn |   0.6449   -0.1173   -0.1935    0.0442 |      0.4959  
     multiethnic |   0.4763    0.0369   -0.2407   -0.2192 |      0.6501  
      monoethnic |   0.2085   -0.0207    0.2968    0.3299 |      0.7877  
       heirparty |   0.4866   -0.2386   -0.4901   -0.0024 |      0.3762  
      heirfamily |  -0.3780   -0.0644    0.5892    0.1037 |      0.4796  
      legcompetn |  -0.1172    0.0264    0.0074   -0.3865 |      0.8398  
    leaderrela~s |  -0.1597   -0.1222    0.4854    0.1421 |      0.7196  
       leaderciv |  -0.1602   -0.7853   -0.1629   -0.3860 |      0.1645  
       leadermil |   0.0870    0.9203    0.0898   -0.1422 |      0.1062  
     leaderrebel |   0.1243   -0.1228    0.1244    0.8213 |      0.3094  
         heirciv |   0.2659   -0.5278   -0.3906   -0.0575 |      0.3942  
          cabciv |  -0.1630   -0.4460   -0.0258    0.0282 |      0.7802  
          cabmil |   0.1587    0.5301   -0.0137    0.0133 |      0.7071  
      partymilit |   0.3450   -0.2262   -0.4249    0.3400 |      0.4674  
       ldrPriorD |   0.1235   -0.3801    0.1847   -0.3691 |      0.6717  
        ldrParty |   0.1814   -0.1634   -0.5181   -0.1046 |      0.6194  
          ldrMil |  -0.0124    0.7787    0.0839   -0.1447 |      0.3466  
        ldrRebel |   0.1080   -0.1320    0.1279    0.7396 |      0.4348  
          ldrCiv |   0.0903   -0.0919    0.0385   -0.1534 |      0.9564  
        ldrOther |   0.0062   -0.0161    0.2531    0.0401 |      0.9367  
        ldrForgn |   0.1334   -0.0285    0.0534    0.0459 |      0.9784  
        ldrHered |  -0.7631   -0.2454    0.1390    0.0037 |      0.3585  
        SeizCoup |   0.0718    0.6724    0.1841   -0.1411 |      0.4585  
       SeizRebel |   0.1176   -0.0994   -0.2792    0.7022 |      0.3779  
       SeizUpris |  -0.0311    0.0583   -0.1005   -0.0860 |      0.9816  
        SeizElec |   0.1646   -0.4170    0.0621   -0.4522 |      0.5734  
        SeizSucc |   0.0151    0.0955    0.1120   -0.0229 |      0.9744  
         SeizFam |  -0.6208   -0.2042    0.0845   -0.0070 |      0.5811  
     PartyhNoWin |   0.1541    0.0133    0.1059    0.0655 |      0.9646  
       PartyhWin |   0.0132    0.0275    0.0407   -0.0421 |      0.9952  
       PartyhReb |   0.1464   -0.0404   -0.3639    0.5540 |      0.5094  
    PartyhPrio~m |   0.3214   -0.3599   -0.0257   -0.3544 |      0.6061  
    PartyhNopa~y |  -0.9454   -0.0252   -0.0405   -0.1163 |      0.1099  
      PartyhElec |  -0.0133   -0.0568    0.1134   -0.0333 |      0.9841  
    MilPartyAlly |   0.0437    0.2375   -0.0222   -0.0453 |      0.9424  
      MilPartyNo |  -0.3679    0.3791   -0.2484   -0.0722 |      0.6838  
    MilPartyPr~r |   0.1560    0.5088   -0.1297   -0.0989 |      0.7209  
      nomilitary |  -0.0299   -0.1099    0.0292   -0.1070 |      0.9756  
    milethnic_~e |  -0.0027    0.0905   -0.4386   -0.0024 |      0.8122  
    milethnic~ro |   0.0218   -0.1080    0.2374   -0.1025 |      0.9261  
    milethnic~mo |  -0.0163    0.0535    0.2771    0.1731 |      0.8895  
    sectyapp_p~y |   0.1048   -0.0398   -0.6272    0.2084 |      0.5196  
    sectyapppers |  -0.0203   -0.0590    0.6873   -0.0128 |      0.5333  
    ElecldrPrD~t |  -0.0273   -0.0852    0.0686   -0.0390 |      0.9873  
    ElecldrPrDem |  -0.0806   -0.1238    0.0544   -0.0770 |      0.9719  
      ElecldrNot |   0.0468    0.1378   -0.4260    0.3478 |      0.6756  
       Elecldr1C |   0.3116    0.1452    0.2296   -0.1777 |      0.7957  
       Elecldr1F |   0.0791   -0.0087    0.1056    0.1868 |      0.9528  
     ElecldrMLeg |   0.0844   -0.1422   -0.0790   -0.1191 |      0.9454  
    ElecldrMExec |   0.0253    0.0057    0.0676   -0.1401 |      0.9739  
    legnoms_in~t |  -0.0753   -0.0516   -0.1660    0.4354 |      0.7640  
    legnoms_veto |   0.1588    0.1077    0.1067   -0.3081 |      0.8514  
    legnoms_no~o |  -0.3437   -0.1119   -0.1413   -0.0912 |      0.8529  
    legnoms_pr~m |   0.0058   -0.0662    0.0214   -0.0339 |      0.9942  
      LdrexHighR |   0.0332    0.7868   -0.0658   -0.1746 |      0.3708  
       LdrexLowR |   0.0877    0.3207    0.2580   -0.0213 |      0.8033  
      LdrexRebel |   0.1287   -0.1454    0.1552    0.8189 |      0.3038  
      LdrexDemEl |   0.2037   -0.4069    0.1588   -0.3469 |      0.6429  
      LdrexParty |   0.1649   -0.3072   -0.4602   -0.1068 |      0.5946  
      LdrexLoyal |   0.0029   -0.0222   -0.0408   -0.0818 |      0.9911  
      LdrexReltv |   0.0251   -0.0048    0.1504    0.0338 |      0.9770  
     LdrexRulFam |  -0.7631   -0.2454    0.1390    0.0037 |      0.3585  
      LdrexOther |  -0.1920   -0.0843    0.0577   -0.1210 |      0.9411  
    partye~mpers |   0.4322    0.1620    0.4673   -0.0551 |      0.5779  
    partyexcom~n |   0.1760   -0.1315   -0.3381    0.2057 |      0.7652  
    partyexcom~e |   0.0987   -0.0647   -0.3375   -0.0336 |      0.8591  
     createparty |   0.1573    0.3493    0.4241    0.0053 |      0.6449  
    ---------------------------------------------------------------------

Factor rotation matrix

    --------------------------------------------------
                 | Factor1  Factor2  Factor3  Factor4 
    -------------+------------------------------------
         Factor1 |  0.5668  -0.6106  -0.7005   0.1314 
         Factor2 |  0.7636   0.5747   0.1708   0.0121 
         Factor3 | -0.1415   0.1141   0.0073   0.9905 
         Factor4 |  0.2750  -0.5329   0.6929   0.0379 
    --------------------------------------------------

.                 predict pht1 pht2 pht3 pht4     
(regression scoring assumed)

Scoring coefficients (method = regression; based on promax(3) rotated factors)

    ------------------------------------------------------
        Variable |  Factor1   Factor2   Factor3   Factor4 
    -------------+----------------------------------------
    partyrbrstmp |  0.01362   0.01646   0.01855  -0.01772 
       militrank | -0.01987   0.15504  -0.14152  -0.15661 
     ldrrotation | -0.01385   0.05857  -0.03965  -0.00365 
      milconsult | -0.01974   0.03566  -0.04532  -0.00651 
    milmerit_mil | -0.02665   0.01080  -0.13378  -0.10708 
    milmeritpers |  0.00747   0.01067   0.07106   0.01503 
      milnotrial |  0.00778   0.00603   0.03048  -0.00461 
      plebiscite |  0.00505  -0.02780   0.02019  -0.00296 
        heirclan |  0.00216  -0.00395   0.00710  -0.00343 
      officepers |  0.00796  -0.00258   0.08032  -0.01642 
     paramilpers | -0.02090  -0.00286   0.04650  -0.00653 
    ParamilParty |  0.02101  -0.00368  -0.04028  -0.02916 
     ParamilFReb | -0.01471   0.01116  -0.02738   0.01641 
    supportparty |  0.00000   0.00000   0.00000   0.00000 
     partyleader |  0.00450  -0.01574   0.00261  -0.02306 
     localorgzns |  0.04153  -0.00695  -0.00838   0.02943 
       partymins |  0.03772  -0.04186  -0.00526   0.03373 
       excomcivn |  0.04066   0.02464  -0.02405  -0.01909 
     multiethnic |  0.11119  -0.06765  -0.00158   0.00012 
      monoethnic |  0.06718  -0.07080   0.08116   0.14067 
       heirparty |  0.04668  -0.04976  -0.07637   0.02305 
      heirfamily | -0.02035   0.01286   0.08035  -0.00753 
      legcompetn | -0.01984   0.01577   0.00135  -0.07526 
    leaderrela~s | -0.00982  -0.00951   0.03075   0.00147 
       leaderciv |  0.00000   0.00000   0.00000   0.00000 
       leadermil |  0.10837   0.31156   0.13731   0.27352 
     leaderrebel | -0.01378   0.05613   0.03347   0.25006 
         heirciv |  0.04026  -0.02275  -0.05628  -0.01285 
          cabciv | -0.01965  -0.02935  -0.01668  -0.01727 
          cabmil |  0.01737   0.09672  -0.01383  -0.00739 
      partymilit |  0.03648  -0.01025  -0.02460   0.03057 
       ldrPriorD |  0.08005  -0.17765   0.06603  -0.09548 
        ldrParty |  0.07678  -0.17976  -0.06091  -0.02381 
          ldrMil |  0.00000   0.00000   0.00000   0.00000 
        ldrRebel |  0.06912  -0.12648   0.05738   0.15003 
          ldrCiv |  0.02685  -0.07446   0.01585  -0.04899 
        ldrOther |  0.02222  -0.07209   0.05282   0.02231 
        ldrForgn |  0.05180  -0.10373   0.00949   0.00581 
        ldrHered | -0.26171  -0.18231   0.08206   0.11201 
        SeizCoup | -0.00057   0.00425  -0.00252  -0.02349 
       SeizRebel | -0.00952   0.01309  -0.08130   0.12350 
       SeizUpris |  0.01134  -0.00145   0.00213   0.00286 
        SeizElec |  0.02826  -0.05455   0.01485  -0.04500 
        SeizSucc |  0.00164  -0.01906   0.00532   0.01120 
         SeizFam | -0.00759  -0.01578   0.00320   0.00943 
     PartyhNoWin | -0.01401  -0.01809   0.00487   0.01029 
       PartyhWin | -0.03679  -0.00296  -0.01215  -0.01208 
       PartyhReb | -0.04852  -0.02660  -0.09930   0.05581 
    PartyhPrio~m | -0.00785  -0.05702  -0.03794  -0.11290 
    PartyhNopa~y | -0.47572  -0.03981  -0.08198   0.01375 
      PartyhElec | -0.02686  -0.00814   0.01643   0.00746 
    MilPartyAlly | -0.00798  -0.02270   0.00682   0.01901 
      MilPartyNo | -0.02133  -0.02236  -0.00473  -0.00380 
    MilPartyPr~r | -0.01011  -0.00820   0.00406  -0.00242 
      nomilitary | -0.00201  -0.02507   0.01157  -0.03316 
    milethnic_~e |  0.00000   0.00000   0.00000   0.00000 
    milethnic~ro |  0.01751  -0.04476   0.13303   0.00339 
    milethnic~mo |  0.00186  -0.01098   0.10056   0.03340 
    sectyapp_p~y |  0.00164  -0.00396  -0.06870   0.04139 
    sectyapppers |  0.01332  -0.02575   0.10440  -0.00942 
    ElecldrPrD~t | -0.01918  -0.02279   0.01043   0.01351 
    ElecldrPrDem | -0.02902  -0.03347  -0.00235   0.00096 
      ElecldrNot | -0.06286   0.00835  -0.12712   0.17055 
       Elecldr1C |  0.00000   0.00000   0.00000   0.00000 
       Elecldr1F | -0.01375  -0.01255   0.00108   0.08198 
     ElecldrMLeg | -0.03619  -0.05008  -0.04756   0.02032 
    ElecldrMExec | -0.04598  -0.03673  -0.02342   0.00746 
    legnoms_in~t | -0.01969  -0.00588  -0.01542   0.01771 
    legnoms_veto |  0.02419  -0.00165   0.02772  -0.05405 
    legnoms_no~o | -0.05157  -0.02310  -0.02334  -0.00482 
    legnoms_pr~m |  0.00099  -0.00133  -0.00134  -0.00290 
      LdrexHighR | -0.05176   0.24032   0.02177  -0.11531 
       LdrexLowR | -0.03022   0.12693   0.02889  -0.11406 
      LdrexRebel | -0.02270   0.03104   0.06418   0.18679 
      LdrexDemEl |  0.01238  -0.02782   0.06343  -0.07442 
      LdrexParty |  0.00000   0.00000   0.00000   0.00000 
      LdrexLoyal | -0.01507   0.02355   0.00218  -0.02387 
      LdrexReltv | -0.02347   0.01215   0.05519   0.02324 
     LdrexRulFam |  0.00000   0.00000   0.00000   0.00000 
      LdrexOther | -0.03862   0.00543   0.01919  -0.00806 
    partye~mpers |  0.08114  -0.02198   0.12395  -0.03354 
    partyexcom~n |  0.00389  -0.02761  -0.02374   0.02534 
    partyexcom~e |  0.00129  -0.02720  -0.02122  -0.02000 
     createparty | -0.04572  -0.06754   0.06614   0.01829 
    ------------------------------------------------------


.                 factor $allvar1 $allvar2 $allvar3 $allvar4 $allvar5 $allvar6 $allvar
> 7 $allvar8 $allvar9 $allvar10 ht_parties ht_party ht_mil, factors(4)                
(obs=2,085)
(collinear variables specified)

Factor analysis/correlation                      Number of obs    =      2,085
    Method: principal factors                    Retained factors =          4
    Rotation: (unrotated)                        Number of params =        342

    --------------------------------------------------------------------------
         Factor  |   Eigenvalue   Difference        Proportion   Cumulative
    -------------+------------------------------------------------------------
        Factor1  |     10.21290      2.27055            0.1390       0.1390
        Factor2  |      7.94235      2.40732            0.1081       0.2472
        Factor3  |      5.53503      0.24490            0.0754       0.3225
        Factor4  |      5.29012      1.80580            0.0720       0.3946
        Factor5  |      3.48433      0.49936            0.0474       0.4420
        Factor6  |      2.98497      0.34068            0.0406       0.4826
        Factor7  |      2.64429      0.16355            0.0360       0.5186
        Factor8  |      2.48074      0.30452            0.0338       0.5524
        Factor9  |      2.17622      0.04559            0.0296       0.5820
       Factor10  |      2.13063      0.28073            0.0290       0.6111
       Factor11  |      1.84989      0.04418            0.0252       0.6362
       Factor12  |      1.80571      0.15691            0.0246       0.6608
       Factor13  |      1.64880      0.02326            0.0224       0.6833
       Factor14  |      1.62554      0.11431            0.0221       0.7054
       Factor15  |      1.51122      0.15646            0.0206       0.7260
       Factor16  |      1.35476      0.08480            0.0184       0.7444
       Factor17  |      1.26996      0.06997            0.0173       0.7617
       Factor18  |      1.19999      0.03624            0.0163       0.7781
       Factor19  |      1.16375      0.05319            0.0158       0.7939
       Factor20  |      1.11056      0.04449            0.0151       0.8090
       Factor21  |      1.06607      0.04885            0.0145       0.8235
       Factor22  |      1.01722      0.07020            0.0138       0.8374
       Factor23  |      0.94702      0.05727            0.0129       0.8503
       Factor24  |      0.88975      0.02894            0.0121       0.8624
       Factor25  |      0.86082      0.02532            0.0117       0.8741
       Factor26  |      0.83550      0.02724            0.0114       0.8855
       Factor27  |      0.80826      0.07228            0.0110       0.8965
       Factor28  |      0.73598      0.02941            0.0100       0.9065
       Factor29  |      0.70657      0.01978            0.0096       0.9161
       Factor30  |      0.68680      0.02710            0.0094       0.9255
       Factor31  |      0.65969      0.06776            0.0090       0.9345
       Factor32  |      0.59194      0.01619            0.0081       0.9425
       Factor33  |      0.57575      0.04056            0.0078       0.9504
       Factor34  |      0.53518      0.04667            0.0073       0.9576
       Factor35  |      0.48852      0.05445            0.0067       0.9643
       Factor36  |      0.43406      0.04172            0.0059       0.9702
       Factor37  |      0.39234      0.02254            0.0053       0.9755
       Factor38  |      0.36980      0.01712            0.0050       0.9806
       Factor39  |      0.35268      0.03190            0.0048       0.9854
       Factor40  |      0.32079      0.01788            0.0044       0.9897
       Factor41  |      0.30291      0.05596            0.0041       0.9939
       Factor42  |      0.24695      0.00741            0.0034       0.9972
       Factor43  |      0.23953      0.03588            0.0033       1.0005
       Factor44  |      0.20366      0.01157            0.0028       1.0033
       Factor45  |      0.19209      0.02310            0.0026       1.0059
       Factor46  |      0.16899      0.01403            0.0023       1.0082
       Factor47  |      0.15496      0.01213            0.0021       1.0103
       Factor48  |      0.14283      0.03598            0.0019       1.0122
       Factor49  |      0.10685      0.00805            0.0015       1.0137
       Factor50  |      0.09880      0.00539            0.0013       1.0150
       Factor51  |      0.09341      0.01406            0.0013       1.0163
       Factor52  |      0.07935      0.00554            0.0011       1.0174
       Factor53  |      0.07381      0.00846            0.0010       1.0184
       Factor54  |      0.06535      0.01170            0.0009       1.0193
       Factor55  |      0.05365      0.01969            0.0007       1.0200
       Factor56  |      0.03396      0.01226            0.0005       1.0205
       Factor57  |      0.02170      0.00523            0.0003       1.0208
       Factor58  |      0.01648      0.01165            0.0002       1.0210
       Factor59  |      0.00482      0.00212            0.0001       1.0211
       Factor60  |      0.00270      0.00270            0.0000       1.0211
       Factor61  |      0.00000      0.00000            0.0000       1.0211
       Factor62  |      0.00000      0.00000            0.0000       1.0211
       Factor63  |      0.00000      0.00000            0.0000       1.0211
       Factor64  |     -0.00000      0.00000           -0.0000       1.0211
       Factor65  |     -0.00000      0.00000           -0.0000       1.0211
       Factor66  |     -0.00000      0.00000           -0.0000       1.0211
       Factor67  |     -0.00000      0.00353           -0.0000       1.0211
       Factor68  |     -0.00353      0.00120           -0.0000       1.0211
       Factor69  |     -0.00473      0.00928           -0.0001       1.0210
       Factor70  |     -0.01401      0.00526           -0.0002       1.0208
       Factor71  |     -0.01927      0.00417           -0.0003       1.0205
       Factor72  |     -0.02344      0.01788           -0.0003       1.0202
       Factor73  |     -0.04132      0.00809           -0.0006       1.0197
       Factor74  |     -0.04940      0.00900           -0.0007       1.0190
       Factor75  |     -0.05840      0.00338           -0.0008       1.0182
       Factor76  |     -0.06178      0.01424           -0.0008       1.0173
       Factor77  |     -0.07602      0.01180           -0.0010       1.0163
       Factor78  |     -0.08782      0.00454           -0.0012       1.0151
       Factor79  |     -0.09237      0.00811           -0.0013       1.0139
       Factor80  |     -0.10048      0.00427           -0.0014       1.0125
       Factor81  |     -0.10474      0.00495           -0.0014       1.0111
       Factor82  |     -0.10969      0.01011           -0.0015       1.0096
       Factor83  |     -0.11980      0.00578           -0.0016       1.0079
       Factor84  |     -0.12558      0.00937           -0.0017       1.0062
       Factor85  |     -0.13495      0.00984           -0.0018       1.0044
       Factor86  |     -0.14479      0.03296           -0.0020       1.0024
       Factor87  |     -0.17775            .           -0.0024       1.0000
    --------------------------------------------------------------------------
    LR test: independent vs. saturated: chi2(3741)= 6.5e+05 Prob>chi2 = 0.0000

Factor loadings (pattern matrix) and unique variances

    ---------------------------------------------------------------------
        Variable |  Factor1   Factor2   Factor3   Factor4 |   Uniqueness 
    -------------+----------------------------------------+--------------
    partyrbrstmp |  -0.1706    0.4628   -0.1544    0.3078 |      0.6381  
       militrank |  -0.5147    0.5632   -0.0671   -0.4717 |      0.1909  
     ldrrotation |  -0.1707    0.1146   -0.0888   -0.4525 |      0.7451  
      milconsult |  -0.2245    0.2261   -0.0011   -0.4454 |      0.7001  
    milmerit_mil |   0.3596   -0.1655   -0.3514   -0.4405 |      0.5258  
    milmeritpers |  -0.3354    0.1768    0.3414    0.3934 |      0.5849  
      milnotrial |  -0.2722    0.3303    0.3052    0.3487 |      0.6021  
      plebiscite |  -0.1782    0.2888   -0.3248    0.1125 |      0.7667  
        heirclan |  -0.1935   -0.1419    0.3240    0.1869 |      0.8025  
      officepers |  -0.5043    0.0268   -0.0842    0.5251 |      0.4622  
     paramilpers |  -0.4143    0.0479   -0.0316    0.4055 |      0.6607  
    ParamilParty |   0.3870    0.1214   -0.0104   -0.0723 |      0.8301  
     ParamilFReb |   0.0037    0.0078    0.1648   -0.2783 |      0.8953  
    supportparty |   0.5091    0.7377    0.0388    0.2891 |      0.1114  
     partyleader |   0.3982    0.3844    0.0837    0.3234 |      0.5821  
     localorgzns |   0.5358    0.6638    0.0005    0.2218 |      0.2231  
       partymins |   0.5512    0.5240    0.1069    0.1390 |      0.3908  
       excomcivn |   0.5771    0.3953   -0.0219    0.1089 |      0.4983  
     multiethnic |   0.3973    0.3359   -0.2391   -0.0389 |      0.6706  
      monoethnic |  -0.0437    0.2022    0.2927    0.2671 |      0.8002  
       heirparty |   0.7595    0.1528   -0.0540   -0.0820 |      0.3902  
      heirfamily |  -0.5688   -0.2417    0.1329    0.3416 |      0.4838  
      legcompetn |  -0.1073   -0.0249   -0.4716   -0.0516 |      0.7628  
    leaderrela~s |  -0.3366   -0.1285    0.1564    0.3599 |      0.7162  
       leaderciv |   0.4667   -0.5918   -0.4387    0.2434 |      0.1803  
       leadermil |  -0.5996    0.6150   -0.0487   -0.3807 |      0.1150  
     leaderrebel |   0.1511    0.0239    0.7660    0.1796 |      0.3576  
         heirciv |   0.7393   -0.1639   -0.1276    0.0708 |      0.4052  
          cabciv |   0.2087   -0.3799   -0.0289    0.1593 |      0.7859  
          cabmil |  -0.2307    0.4203    0.0743   -0.2319 |      0.7109  
      partymilit |   0.6671    0.0463    0.3054   -0.0789 |      0.4533  
       ldrPriorD |   0.1406   -0.0865   -0.4027    0.3624 |      0.6792  
        ldrParty |   0.5557   -0.0345   -0.1452   -0.2265 |      0.6177  
          ldrMil |  -0.5662    0.4618   -0.0674   -0.3452 |      0.3424  
        ldrRebel |   0.1367    0.0140    0.6715    0.1790 |      0.4982  
          ldrCiv |   0.0663    0.0211   -0.1598    0.1031 |      0.9590  
        ldrOther |  -0.1581    0.0454    0.0374    0.1856 |      0.9371  
        ldrForgn |   0.0603    0.0846    0.0609    0.0970 |      0.9761  
        ldrHered |  -0.3846   -0.7191    0.0671    0.0062 |      0.3304  
        SeizCoup |  -0.5230    0.4821   -0.0757   -0.1959 |      0.4499  
       SeizRebel |   0.3963   -0.0277    0.6512   -0.1186 |      0.4041  
       SeizUpris |   0.0116   -0.0004   -0.0882   -0.1078 |      0.9805  
        SeizElec |   0.2631   -0.0921   -0.5017    0.3061 |      0.5769  
        SeizSucc |  -0.1312    0.0872   -0.0162    0.0323 |      0.9739  
         SeizFam |  -0.2917   -0.5902    0.0423   -0.0131 |      0.5646  
     PartyhNoWin |   0.0097    0.1370    0.0837    0.1168 |      0.9605  
       PartyhWin |  -0.0404    0.0395   -0.0487    0.0166 |      0.9942  
       PartyhReb |   0.4174    0.0164    0.5175   -0.1977 |      0.5186  
    PartyhPrio~m |   0.3866    0.0345   -0.3975    0.2628 |      0.6223  
    PartyhNopa~y |  -0.5091   -0.7377   -0.0388   -0.2891 |      0.1114  
      PartyhElec |  -0.0502   -0.0274   -0.0456    0.1073 |      0.9831  
    MilPartyAlly |  -0.1054    0.1657   -0.0367   -0.1223 |      0.9451  
      MilPartyNo |  -0.2882   -0.0952   -0.0013   -0.4797 |      0.6777  
    MilPartyPr~r |  -0.1479    0.3926   -0.0546   -0.3043 |      0.7284  
      nomilitary |   0.0221   -0.0738   -0.1212    0.0676 |      0.9748  
    milethnic_~e |   0.2552   -0.0266   -0.0022   -0.3465 |      0.8141  
    milethnic~ro |  -0.1039   -0.0115   -0.0897    0.2274 |      0.9293  
    milethnic~mo |  -0.2190    0.0746    0.1601    0.1520 |      0.8977  
    sectyapp_p~y |   0.5369   -0.0494    0.1992   -0.3901 |      0.5174  
    sectyapppers |  -0.4438    0.0567   -0.0185    0.5117 |      0.5377  
    ElecldrPrD~t |  -0.0106   -0.0540   -0.0523    0.0840 |      0.9872  
    ElecldrPrDem |  -0.0096   -0.1115   -0.1018    0.0761 |      0.9713  
      ElecldrNot |   0.2600    0.0276    0.3953   -0.3554 |      0.6491  
       Elecldr1C |  -0.0965    0.3529   -0.1338    0.1847 |      0.8142  
       Elecldr1F |   0.0041    0.0575    0.1854    0.1083 |      0.9506  
     ElecldrMLeg |   0.1802   -0.0133   -0.1748    0.0332 |      0.9357  
    ElecldrMExec |  -0.0345    0.0600   -0.2247    0.0438 |      0.9428  
    legnoms_in~t |   0.1441   -0.1334    0.4490   -0.1141 |      0.7468  
    legnoms_veto |  -0.0748    0.2200   -0.3416    0.0643 |      0.8252  
    legnoms_no~o |  -0.0325   -0.3297   -0.1139   -0.1476 |      0.8555  
    legnoms_pr~m |   0.0264   -0.0270   -0.0444    0.0494 |      0.9942  
      LdrexHighR |  -0.4439    0.4800   -0.0963   -0.4397 |      0.3700  
       LdrexLowR |  -0.3327    0.2862    0.0160    0.0441 |      0.8052  
      LdrexRebel |   0.1472    0.0218    0.7528    0.2126 |      0.3660  
      LdrexDemEl |   0.2202   -0.0478   -0.3841    0.3802 |      0.6572  
      LdrexParty |   0.5917   -0.1203   -0.1472   -0.1167 |      0.6001  
      LdrexLoyal |   0.0355   -0.0063   -0.1008   -0.0198 |      0.9882  
      LdrexReltv |  -0.0805    0.0360    0.0341    0.1183 |      0.9771  
     LdrexRulFam |  -0.3846   -0.7191    0.0671    0.0062 |      0.3304  
      LdrexOther |  -0.0986   -0.1674   -0.1448    0.0276 |      0.9405  
    partye~mpers |  -0.1841    0.4867   -0.0338    0.3785 |      0.5849  
    partyexcom~n |   0.4355    0.0059    0.1602   -0.1252 |      0.7690  
    partyexcom~e |   0.3275   -0.0105   -0.0688   -0.1783 |      0.8561  
     createparty |  -0.4169    0.3911    0.0251    0.1657 |      0.6452  
      ht_parties |   0.0810    0.1995   -0.4177   -0.0757 |      0.7734  
        ht_party |   0.3827    0.4902   -0.2299    0.0234 |      0.5599  
     ht_military |  -0.4576    0.4146    0.1316   -0.1479 |      0.5795  
    ---------------------------------------------------------------------

.                 rotate, promax(3)  

Factor analysis/correlation                      Number of obs    =      2,085
    Method: principal factors                    Retained factors =          4
    Rotation: oblique promax (Kaiser off)        Number of params =        342

    --------------------------------------------------------------------------
         Factor  |     Variance   Proportion    Rotated factors are correlated
    -------------+------------------------------------------------------------
        Factor1  |      8.48448       0.1155
        Factor2  |      8.17187       0.1113
        Factor3  |      7.65760       0.1043
        Factor4  |      5.59181       0.0761
    --------------------------------------------------------------------------
    LR test: independent vs. saturated: chi2(3741)= 6.5e+05 Prob>chi2 = 0.0000

Rotated factor loadings (pattern matrix) and unique variances

    ---------------------------------------------------------------------
        Variable |  Factor1   Factor2   Factor3   Factor4 |   Uniqueness 
    -------------+----------------------------------------+--------------
    partyrbrstmp |   0.3918    0.1673    0.4008   -0.1545 |      0.6381  
       militrank |   0.0559    0.8978   -0.0551   -0.1471 |      0.1909  
     ldrrotation |  -0.1309    0.4333   -0.2826   -0.1300 |      0.7451  
      milconsult |  -0.0670    0.5314   -0.2281   -0.0480 |      0.7001  
    milmerit_mil |  -0.0826   -0.0360   -0.6131   -0.3272 |      0.5258  
    milmeritpers |   0.0895    0.0593    0.5606    0.3182 |      0.5849  
      milnotrial |   0.2334    0.1468    0.5043    0.2898 |      0.6021  
      plebiscite |   0.1897    0.1732    0.2073   -0.3362 |      0.7667  
        heirclan |  -0.1631   -0.0811    0.2680    0.3050 |      0.8025  
      officepers |  -0.0655   -0.0617    0.7196   -0.1211 |      0.4622  
     paramilpers |  -0.0417   -0.0158    0.5747   -0.0631 |      0.6607  
    ParamilParty |   0.2710   -0.0777   -0.2591    0.0360 |      0.8301  
     ParamilFReb |  -0.0828    0.1894   -0.2269    0.1509 |      0.8953  
    supportparty |   0.9463    0.0171    0.0586    0.1247 |      0.1114  
     partyleader |   0.6119   -0.1618    0.1085    0.1530 |      0.5821  
     localorgzns |   0.8794   -0.0027   -0.0245    0.0858 |      0.2231  
       partymins |   0.7445   -0.0367   -0.1134    0.1878 |      0.3908  
       excomcivn |   0.6465   -0.1200   -0.1769    0.0602 |      0.4983  
     multiethnic |   0.4675    0.0108   -0.2250   -0.1858 |      0.6706  
      monoethnic |   0.2186    0.0019    0.2937    0.3005 |      0.8002  
       heirparty |   0.4816   -0.2450   -0.4701    0.0391 |      0.3902  
      heirfamily |  -0.3796   -0.0629    0.5804    0.0746 |      0.4838  
      legcompetn |  -0.0779    0.0356   -0.0181   -0.4846 |      0.7628  
    leaderrela~s |  -0.1657   -0.1210    0.4842    0.1292 |      0.7162  
       leaderciv |  -0.1611   -0.7866   -0.1481   -0.3726 |      0.1803  
       leadermil |   0.0834    0.9173    0.0761   -0.1344 |      0.1150  
     leaderrebel |   0.1312   -0.1162    0.1214    0.7887 |      0.3576  
         heirciv |   0.2627   -0.5306   -0.3723   -0.0329 |      0.4052  
          cabciv |  -0.1547   -0.4415   -0.0267    0.0006 |      0.7859  
          cabmil |   0.1528    0.5263   -0.0151    0.0387 |      0.7109  
      partymilit |   0.3400   -0.2382   -0.4070    0.3834 |      0.4533  
       ldrPriorD |   0.1235   -0.3817    0.1948   -0.3660 |      0.6792  
        ldrParty |   0.1840   -0.1732   -0.5097   -0.0861 |      0.6177  
          ldrMil |  -0.0133    0.7825    0.0682   -0.1490 |      0.3424  
        ldrRebel |   0.1182   -0.1219    0.1215    0.6929 |      0.4982  
          ldrCiv |   0.0868   -0.0973    0.0436   -0.1452 |      0.9590  
        ldrOther |   0.0145   -0.0049    0.2520    0.0269 |      0.9371  
        ldrForgn |   0.1279   -0.0347    0.0634    0.0736 |      0.9761  
        ldrHered |  -0.7793   -0.2455    0.1330    0.0105 |      0.3304  
        SeizCoup |   0.0717    0.6791    0.1733   -0.1443 |      0.4499  
       SeizRebel |   0.1223   -0.0950   -0.2795    0.6903 |      0.4041  
       SeizUpris |  -0.0258    0.0546   -0.1035   -0.0914 |      0.9805  
        SeizElec |   0.1654   -0.4197    0.0729   -0.4518 |      0.5769  
        SeizSucc |   0.0158    0.0989    0.1087   -0.0299 |      0.9739  
         SeizFam |  -0.6330   -0.2033    0.0798   -0.0019 |      0.5646  
     PartyhNoWin |   0.1508    0.0127    0.1157    0.0915 |      0.9605  
       PartyhWin |   0.0183    0.0308    0.0379   -0.0522 |      0.9942  
       PartyhReb |   0.1477   -0.0393   -0.3613    0.5569 |      0.5186  
    PartyhPrio~m |   0.3144   -0.3697   -0.0095   -0.3334 |      0.6223  
    PartyhNopa~y |  -0.9463   -0.0171   -0.0586   -0.1247 |      0.1114  
      PartyhElec |  -0.0130   -0.0618    0.1124   -0.0467 |      0.9831  
    MilPartyAlly |   0.0451    0.2291   -0.0284   -0.0536 |      0.9451  
      MilPartyNo |  -0.3717    0.3877   -0.2612   -0.0615 |      0.6777  
    MilPartyPr~r |   0.1526    0.5027   -0.1335   -0.0828 |      0.7284  
      nomilitary |  -0.0249   -0.1081    0.0287   -0.1152 |      0.9748  
    milethnic_~e |  -0.0015    0.0702   -0.4372    0.0127 |      0.8141  
    milethnic~ro |   0.0117   -0.1032    0.2428   -0.0913 |      0.9293  
    milethnic~mo |  -0.0059    0.0744    0.2681    0.1399 |      0.8977  
    sectyapp_p~y |   0.1024   -0.0444   -0.6183    0.2456 |      0.5174  
    sectyapppers |  -0.0165   -0.0607    0.6829   -0.0485 |      0.5377  
    ElecldrPrD~t |  -0.0218   -0.0843    0.0673   -0.0499 |      0.9872  
    ElecldrPrDem |  -0.0694   -0.1190    0.0499   -0.1000 |      0.9713  
      ElecldrNot |   0.0319    0.1369   -0.4156    0.4083 |      0.6491  
       Elecldr1C |   0.3004    0.1406    0.2438   -0.1320 |      0.8142  
       Elecldr1F |   0.0778   -0.0203    0.1084    0.1905 |      0.9506  
     ElecldrMLeg |   0.0945   -0.1338   -0.0832   -0.1497 |      0.9357  
    ElecldrMExec |   0.0511    0.0100    0.0490   -0.2247 |      0.9428  
    legnoms_in~t |  -0.0838   -0.0500   -0.1633    0.4570 |      0.7468  
    legnoms_veto |   0.1708    0.1072    0.1004   -0.3430 |      0.8252  
    legnoms_no~o |  -0.3283   -0.1027   -0.1546   -0.1281 |      0.8555  
    legnoms_pr~m |   0.0077   -0.0642    0.0214   -0.0387 |      0.9942  
      LdrexHighR |   0.0342    0.7884   -0.0785   -0.1667 |      0.3700  
       LdrexLowR |   0.0798    0.3188    0.2551   -0.0202 |      0.8052  
      LdrexRebel |   0.1382   -0.1372    0.1504    0.7766 |      0.3660  
      LdrexDemEl |   0.2000   -0.4081    0.1721   -0.3363 |      0.6572  
      LdrexParty |   0.1663   -0.3131   -0.4468   -0.0793 |      0.6001  
      LdrexLoyal |   0.0091   -0.0172   -0.0434   -0.0967 |      0.9882  
      LdrexReltv |   0.0249   -0.0083    0.1510    0.0299 |      0.9771  
     LdrexRulFam |  -0.7793   -0.2455    0.1330    0.0105 |      0.3304  
      LdrexOther |  -0.1737   -0.0811    0.0480   -0.1568 |      0.9405  
    partye~mpers |   0.4234    0.1538    0.4788   -0.0326 |      0.5849  
    partyexcom~n |   0.1801   -0.1274   -0.3337    0.2076 |      0.7690  
    partyexcom~e |   0.1021   -0.0667   -0.3362   -0.0361 |      0.8561  
     createparty |   0.1607    0.3508    0.4176   -0.0146 |      0.6452  
      ht_parties |   0.1907    0.0972   -0.1110   -0.4061 |      0.7734  
        ht_party |   0.6051    0.0749   -0.1449   -0.1737 |      0.5599  
     ht_military |   0.0589    0.5901    0.1832    0.0712 |      0.5795  
    ---------------------------------------------------------------------

Factor rotation matrix

    --------------------------------------------------
                 | Factor1  Factor2  Factor3  Factor4 
    -------------+------------------------------------
         Factor1 |  0.5778  -0.6337  -0.6845   0.1074 
         Factor2 |  0.7642   0.5835   0.1510  -0.0340 
         Factor3 | -0.0947   0.0970   0.0499   0.9936 
         Factor4 |  0.2706  -0.4986   0.7115  -0.0003 
    --------------------------------------------------

.                 predict ht1 ht2 ht3 ht4 
(regression scoring assumed)

Scoring coefficients (method = regression; based on promax(3) rotated factors)

    ------------------------------------------------------
        Variable |  Factor1   Factor2   Factor3   Factor4 
    -------------+----------------------------------------
    partyrbrstmp |  0.01331   0.01697   0.01785  -0.01732 
       militrank | -0.00570   0.14566  -0.12955  -0.15083 
     ldrrotation | -0.00876   0.06800  -0.04202  -0.00306 
      milconsult | -0.01573   0.03799  -0.04191  -0.00570 
    milmerit_mil | -0.02269   0.00551  -0.13400  -0.10612 
    milmeritpers |  0.00339   0.01200   0.07723   0.02694 
      milnotrial |  0.00749  -0.00422   0.02879  -0.01069 
      plebiscite |  0.00569  -0.02390   0.01782  -0.00650 
        heirclan |  0.00460   0.00099   0.00851  -0.00345 
      officepers |  0.00630  -0.00067   0.08047  -0.01429 
     paramilpers | -0.02560  -0.01332   0.04411  -0.01155 
    ParamilParty |  0.01630  -0.01062  -0.04439  -0.02864 
     ParamilFReb | -0.01714   0.00416  -0.03223   0.01201 
    supportparty |  0.45566   0.04315   0.09463  -0.00053 
     partyleader |  0.00111  -0.02213   0.00024  -0.02243 
     localorgzns |  0.04497  -0.00387  -0.00356   0.02884 
       partymins |  0.03406  -0.04672  -0.01359   0.03328 
       excomcivn |  0.04517   0.03510  -0.01869  -0.00809 
     multiethnic |  0.09995  -0.07283  -0.00385   0.00188 
      monoethnic |  0.06435  -0.05748   0.08095   0.13451 
       heirparty |  0.04233  -0.05219  -0.07838   0.02197 
      heirfamily | -0.02497   0.00505   0.07604  -0.01041 
      legcompetn | -0.00953   0.00219  -0.00690  -0.11975 
    leaderrela~s | -0.00902  -0.00694   0.03056   0.00206 
       leaderciv |  0.00000   0.00000   0.00000   0.00000 
       leadermil |  0.08881   0.29213   0.11966   0.25780 
     leaderrebel | -0.01381   0.05265   0.03136   0.23109 
         heirciv |  0.03367  -0.03072  -0.05675  -0.01132 
          cabciv | -0.01630  -0.02297  -0.01217  -0.01303 
          cabmil |  0.02106   0.10776  -0.00624   0.00852 
      partymilit |  0.02870  -0.02247  -0.03107   0.02864 
       ldrPriorD |  0.07724  -0.16668   0.06866  -0.08669 
        ldrParty |  0.07183  -0.17591  -0.05785  -0.01769 
          ldrMil |  0.00000   0.00000   0.00000   0.00000 
        ldrRebel |  0.06621  -0.12139   0.06173   0.14226 
          ldrCiv |  0.02236  -0.07735   0.01435  -0.04410 
        ldrOther |  0.02821  -0.05330   0.05847   0.02699 
        ldrForgn |  0.05019  -0.09045   0.01506   0.02003 
        ldrHered | -0.25581  -0.18410   0.09189   0.10465 
        SeizCoup |  0.00988   0.03058   0.00470  -0.01209 
       SeizRebel | -0.00708   0.02408  -0.07773   0.12390 
       SeizUpris |  0.01003   0.00015   0.00166   0.00420 
        SeizElec |  0.02763  -0.04260   0.01526  -0.03902 
        SeizSucc |  0.00209  -0.01971   0.00695   0.00977 
         SeizFam | -0.00886  -0.01081   0.00300   0.01022 
     PartyhNoWin | -0.01264  -0.00735   0.00483   0.01527 
       PartyhWin | -0.03610  -0.00518  -0.01664  -0.01794 
       PartyhReb | -0.04156  -0.01777  -0.09250   0.05792 
    PartyhPrio~m | -0.00777  -0.05300  -0.03596  -0.10306 
    PartyhNopa~y |  0.00000   0.00000   0.00000   0.00000 
      PartyhElec | -0.02894  -0.01606   0.01232   0.00010 
    MilPartyAlly | -0.01437  -0.03317  -0.00076   0.01057 
      MilPartyNo | -0.02199  -0.02414  -0.00481  -0.00750 
    MilPartyPr~r | -0.01831  -0.02182  -0.00470  -0.00821 
      nomilitary | -0.00024  -0.02196   0.01183  -0.03351 
    milethnic_~e |  0.00000   0.00000   0.00000   0.00000 
    milethnic~ro |  0.01542  -0.02958   0.13340   0.00829 
    milethnic~mo |  0.00605   0.00357   0.09974   0.02822 
    sectyapp_p~y |  0.00113  -0.00406  -0.06930   0.04000 
    sectyapppers |  0.01342  -0.02161   0.10291  -0.01070 
    ElecldrPrD~t | -0.01587  -0.02285   0.01073   0.00706 
    ElecldrPrDem | -0.02353  -0.03415  -0.00167  -0.00922 
      ElecldrNot | -0.06168   0.00640  -0.12490   0.16478 
       Elecldr1C |  0.00000   0.00000   0.00000   0.00000 
       Elecldr1F | -0.01622  -0.02310  -0.00017   0.07178 
     ElecldrMLeg | -0.02751  -0.05186  -0.04317   0.00175 
    ElecldrMExec | -0.03120  -0.04322  -0.01840  -0.02581 
    legnoms_in~t | -0.01802  -0.01070  -0.01557   0.01464 
    legnoms_veto |  0.02635   0.00495   0.03158  -0.04031 
    legnoms_no~o | -0.04447  -0.01651  -0.02092   0.00112 
    legnoms_pr~m |  0.00143  -0.00130  -0.00106  -0.00213 
      LdrexHighR | -0.03804   0.24967   0.03100  -0.10229 
       LdrexLowR | -0.02048   0.13084   0.04201  -0.10265 
      LdrexRebel | -0.01584   0.02645   0.07176   0.16707 
      LdrexDemEl |  0.00711  -0.03279   0.06066  -0.07492 
      LdrexParty |  0.00000   0.00000   0.00000   0.00000 
      LdrexLoyal | -0.01152   0.02648   0.00164  -0.02622 
      LdrexReltv | -0.02684   0.00230   0.05216   0.01578 
     LdrexRulFam |  0.00000   0.00000   0.00000   0.00000 
      LdrexOther | -0.03336   0.00343   0.01753  -0.01891 
    partye~mpers |  0.06850  -0.02810   0.12041  -0.02756 
    partyexcom~n |  0.00279  -0.02661  -0.02320   0.02368 
    partyexcom~e | -0.00234  -0.03196  -0.02343  -0.02295 
     createparty | -0.04638  -0.06925   0.05851   0.00501 
      ht_parties |  0.00059   0.01335  -0.04814  -0.06509 
        ht_party |  0.07907  -0.00447   0.01700  -0.02932 
     ht_military | -0.02406   0.00840  -0.00160  -0.01072 
    ------------------------------------------------------


.                 pwcorr pr2 ht2

             |      pr2      ht2
-------------+------------------
         pr2 |   1.0000 
         ht2 |   0.9892   1.0000 

.                 pwcorr pr1 ht1

             |      pr1      ht1
-------------+------------------
         pr1 |   1.0000 
         ht1 |   0.9920   1.0000 

.                 drop pht* ht1 ht2 ht3 ht4               

.                 * DPI comparison *
.                 factor $allvar1 $allvar2 $allvar3 $allvar4 $allvar5 $allvar6 $allvar
> 7 $allvar8 $allvar9 $allvar10 if dpi_parties~=., factors(4)
(obs=2,538)
(collinear variables specified)

Factor analysis/correlation                      Number of obs    =      2,538
    Method: principal factors                    Retained factors =          4
    Rotation: (unrotated)                        Number of params =        330

    --------------------------------------------------------------------------
         Factor  |   Eigenvalue   Difference        Proportion   Cumulative
    -------------+------------------------------------------------------------
        Factor1  |     10.98015      3.79700            0.1569       0.1569
        Factor2  |      7.18315      1.28541            0.1027       0.2596
        Factor3  |      5.89774      0.64737            0.0843       0.3439
        Factor4  |      5.25037      2.23341            0.0750       0.4190
        Factor5  |      3.01696      0.35644            0.0431       0.4621
        Factor6  |      2.66052      0.24370            0.0380       0.5001
        Factor7  |      2.41682      0.06570            0.0345       0.5347
        Factor8  |      2.35113      0.25116            0.0336       0.5683
        Factor9  |      2.09996      0.18075            0.0300       0.5983
       Factor10  |      1.91922      0.06378            0.0274       0.6257
       Factor11  |      1.85544      0.21998            0.0265       0.6522
       Factor12  |      1.63545      0.13579            0.0234       0.6756
       Factor13  |      1.49967      0.03744            0.0214       0.6970
       Factor14  |      1.46222      0.10324            0.0209       0.7179
       Factor15  |      1.35899      0.08857            0.0194       0.7374
       Factor16  |      1.27042      0.05935            0.0182       0.7555
       Factor17  |      1.21107      0.08877            0.0173       0.7728
       Factor18  |      1.12230      0.02633            0.0160       0.7889
       Factor19  |      1.09597      0.03808            0.0157       0.8045
       Factor20  |      1.05790      0.01690            0.0151       0.8197
       Factor21  |      1.04100      0.06350            0.0149       0.8345
       Factor22  |      0.97750      0.06147            0.0140       0.8485
       Factor23  |      0.91603      0.04266            0.0131       0.8616
       Factor24  |      0.87337      0.04604            0.0125       0.8741
       Factor25  |      0.82733      0.05985            0.0118       0.8859
       Factor26  |      0.76748      0.02278            0.0110       0.8969
       Factor27  |      0.74470      0.04217            0.0106       0.9075
       Factor28  |      0.70253      0.01536            0.0100       0.9176
       Factor29  |      0.68717      0.06239            0.0098       0.9274
       Factor30  |      0.62478      0.06830            0.0089       0.9363
       Factor31  |      0.55648      0.03005            0.0080       0.9443
       Factor32  |      0.52643      0.03869            0.0075       0.9518
       Factor33  |      0.48774      0.01905            0.0070       0.9588
       Factor34  |      0.46868      0.03201            0.0067       0.9655
       Factor35  |      0.43668      0.02402            0.0062       0.9717
       Factor36  |      0.41266      0.01380            0.0059       0.9776
       Factor37  |      0.39886      0.07665            0.0057       0.9833
       Factor38  |      0.32221      0.01634            0.0046       0.9879
       Factor39  |      0.30587      0.02133            0.0044       0.9923
       Factor40  |      0.28454      0.03763            0.0041       0.9964
       Factor41  |      0.24691      0.01989            0.0035       0.9999
       Factor42  |      0.22702      0.02732            0.0032       1.0031
       Factor43  |      0.19971      0.00517            0.0029       1.0060
       Factor44  |      0.19454      0.02221            0.0028       1.0088
       Factor45  |      0.17232      0.02382            0.0025       1.0112
       Factor46  |      0.14850      0.01873            0.0021       1.0133
       Factor47  |      0.12977      0.01941            0.0019       1.0152
       Factor48  |      0.11037      0.01155            0.0016       1.0168
       Factor49  |      0.09881      0.01258            0.0014       1.0182
       Factor50  |      0.08623      0.02209            0.0012       1.0194
       Factor51  |      0.06414      0.00966            0.0009       1.0203
       Factor52  |      0.05449      0.00633            0.0008       1.0211
       Factor53  |      0.04816      0.01118            0.0007       1.0218
       Factor54  |      0.03698      0.00142            0.0005       1.0223
       Factor55  |      0.03556      0.02782            0.0005       1.0228
       Factor56  |      0.00774      0.00181            0.0001       1.0230
       Factor57  |      0.00593      0.00511            0.0001       1.0230
       Factor58  |      0.00082      0.00016            0.0000       1.0231
       Factor59  |      0.00066      0.00066            0.0000       1.0231
       Factor60  |      0.00000      0.00000            0.0000       1.0231
       Factor61  |      0.00000      0.00000            0.0000       1.0231
       Factor62  |      0.00000      0.00000            0.0000       1.0231
       Factor63  |      0.00000      0.00000            0.0000       1.0231
       Factor64  |     -0.00000      0.00000           -0.0000       1.0231
       Factor65  |     -0.00000      0.00000           -0.0000       1.0231
       Factor66  |     -0.00000      0.00815           -0.0000       1.0231
       Factor67  |     -0.00815      0.00560           -0.0001       1.0229
       Factor68  |     -0.01374      0.01351           -0.0002       1.0227
       Factor69  |     -0.02726      0.00576           -0.0004       1.0224
       Factor70  |     -0.03302      0.01250           -0.0005       1.0219
       Factor71  |     -0.04552      0.01209           -0.0007       1.0212
       Factor72  |     -0.05761      0.01094           -0.0008       1.0204
       Factor73  |     -0.06856      0.01796           -0.0010       1.0194
       Factor74  |     -0.08651      0.00822           -0.0012       1.0182
       Factor75  |     -0.09473      0.00476           -0.0014       1.0168
       Factor76  |     -0.09949      0.00654           -0.0014       1.0154
       Factor77  |     -0.10603      0.00742           -0.0015       1.0139
       Factor78  |     -0.11345      0.00292           -0.0016       1.0123
       Factor79  |     -0.11637      0.00968           -0.0017       1.0106
       Factor80  |     -0.12605      0.00243           -0.0018       1.0088
       Factor81  |     -0.12848      0.01504           -0.0018       1.0070
       Factor82  |     -0.14352      0.00844           -0.0021       1.0049
       Factor83  |     -0.15195      0.04095           -0.0022       1.0028
       Factor84  |     -0.19290            .           -0.0028       1.0000
    --------------------------------------------------------------------------
    LR test: independent vs. saturated: chi2(3486)=       . Prob>chi2 =      .

Factor loadings (pattern matrix) and unique variances

    ---------------------------------------------------------------------
        Variable |  Factor1   Factor2   Factor3   Factor4 |   Uniqueness 
    -------------+----------------------------------------+--------------
    partyrbrstmp |   0.1038    0.4620    0.3466    0.2420 |      0.5971  
       militrank |  -0.5812    0.5526   -0.3481    0.0899 |      0.2277  
     ldrrotation |  -0.2016    0.0814   -0.3124    0.0248 |      0.8545  
      milconsult |  -0.4657    0.2022   -0.4572   -0.0396 |      0.5317  
    milmerit_mil |   0.2012   -0.1873   -0.5560    0.1754 |      0.5845  
    milmeritpers |  -0.2027    0.2221    0.4837   -0.1497 |      0.6532  
      milnotrial |  -0.1648    0.4279    0.3812   -0.1939 |      0.6068  
      plebiscite |   0.0040    0.2847    0.1276    0.3672 |      0.7678  
        heirclan |  -0.1390   -0.1573    0.3170   -0.2967 |      0.7674  
      officepers |  -0.1936    0.0895    0.6340    0.1676 |      0.5245  
     paramilpers |  -0.1863    0.0926    0.5196    0.0336 |      0.6856  
    ParamilParty |   0.3555    0.1117   -0.1666    0.0437 |      0.8314  
     ParamilFReb |  -0.0840    0.0388   -0.2366   -0.2452 |      0.8753  
    supportparty |   0.7319    0.6006    0.1855   -0.0216 |      0.0687  
     partyleader |   0.5812    0.3993    0.2164   -0.0590 |      0.4525  
     localorgzns |   0.7339    0.5474    0.1321    0.0264 |      0.1435  
       partymins |   0.7048    0.4547    0.0546   -0.0582 |      0.2901  
       excomcivn |   0.7225    0.3445    0.0333   -0.0428 |      0.3564  
     multiethnic |   0.5642    0.2407   -0.1122    0.2380 |      0.5545  
      monoethnic |   0.0763    0.3180    0.3116   -0.2936 |      0.7097  
       heirparty |   0.7545    0.0776   -0.2918    0.0295 |      0.3387  
      heirfamily |  -0.3662   -0.2122    0.5781   -0.0668 |      0.4822  
      legcompetn |   0.4021    0.0358    0.0970    0.3774 |      0.6852  
    leaderrela~s |  -0.1550   -0.1294    0.5248   -0.0921 |      0.6753  
       leaderciv |   0.4771   -0.6491    0.0827    0.4308 |      0.1586  
       leadermil |  -0.6451    0.6532   -0.2157    0.0813 |      0.1040  
     leaderrebel |   0.2293    0.0242    0.1953   -0.7870 |      0.2894  
         heirciv |   0.6361   -0.3468   -0.1411    0.1020 |      0.4448  
          cabciv |   0.3876   -0.2991    0.2387   -0.0322 |      0.7023  
          cabmil |  -0.3774    0.3849   -0.2827    0.0075 |      0.6294  
      partymilit |   0.6233    0.0154   -0.2247   -0.2520 |      0.4973  
       ldrPriorD |   0.2252   -0.1053    0.1128    0.4045 |      0.7619  
        ldrParty |   0.5468   -0.0561   -0.2956    0.1307 |      0.5934  
          ldrMil |  -0.6611    0.5126   -0.2479    0.0511 |      0.2362  
        ldrRebel |   0.2187    0.0098    0.1986   -0.7280 |      0.3826  
          ldrCiv |   0.1020    0.0142    0.0352    0.1631 |      0.9616  
        ldrOther |  -0.0406    0.0499    0.2489    0.0618 |      0.9301  
        ldrForgn |   0.1072    0.0791    0.0503   -0.0444 |      0.9778  
        ldrHered |  -0.3248   -0.7221    0.2596    0.0041 |      0.3056  
        SeizCoup |  -0.5729    0.5303   -0.1438    0.0718 |      0.3647  
       SeizRebel |   0.3438   -0.1515   -0.0420   -0.6911 |      0.3795  
       SeizUpris |  -0.0380   -0.0158   -0.0493    0.0830 |      0.9890  
        SeizElec |   0.3545   -0.1267    0.0277    0.4825 |      0.6247  
        SeizSucc |  -0.0425    0.1174    0.1048    0.0607 |      0.9698  
         SeizFam |  -0.2335   -0.5244    0.1900    0.0282 |      0.6336  
     PartyhNoWin |   0.1127    0.1277    0.0988    0.0037 |      0.9612  
       PartyhWin |   0.0116    0.0485    0.0547    0.0548 |      0.9915  
       PartyhReb |   0.4024   -0.0056   -0.1639   -0.5772 |      0.4780  
    PartyhPrio~m |   0.4467    0.0222   -0.0267    0.3842 |      0.6517  
    PartyhNopa~y |  -0.7319   -0.6006   -0.1855    0.0216 |      0.0687  
      PartyhElec |   0.0044   -0.0047    0.1081    0.0600 |      0.9847  
    MilPartyAlly |  -0.0705    0.1947   -0.0703    0.0200 |      0.9518  
      MilPartyNo |  -0.6110    0.0058   -0.4878   -0.0395 |      0.3872  
    MilPartyPr~r |  -0.0275    0.3393   -0.1207    0.1034 |      0.8588  
      nomilitary |   0.0273   -0.1496    0.0699    0.0714 |      0.9669  
    milethnic_~e |   0.1603   -0.0732   -0.3540    0.0895 |      0.8356  
    milethnic~ro |  -0.0381   -0.0441    0.1522    0.0157 |      0.9732  
    milethnic~mo |  -0.1687    0.2049    0.2389   -0.1615 |      0.8464  
    sectyapp_p~y |   0.4178   -0.0525   -0.4068   -0.2802 |      0.5787  
    sectyapppers |  -0.1916    0.0218    0.6451    0.0832 |      0.5397  
    ElecldrPrD~t |   0.0039   -0.0413    0.0618    0.0599 |      0.9909  
    ElecldrPrDem |   0.0200   -0.1123    0.0516    0.1080 |      0.9727  
      ElecldrNot |  -0.1988    0.1429   -0.4195   -0.4149 |      0.5920  
       Elecldr1C |   0.0994    0.3315    0.1922    0.2368 |      0.7872  
       Elecldr1F |   0.1310    0.0543    0.0499   -0.0323 |      0.9764  
     ElecldrMLeg |   0.2150   -0.0357   -0.0425    0.1176 |      0.9369  
    ElecldrMExec |   0.1754    0.0579    0.0818    0.1498 |      0.9368  
    legnoms_in~t |   0.0925   -0.0697   -0.0721   -0.3946 |      0.8257  
    legnoms_veto |   0.2536    0.1779    0.1453    0.3152 |      0.7836  
    legnoms_no~o |   0.1151   -0.2277   -0.0831    0.1552 |      0.9039  
    legnoms_pr~m |  -0.0243   -0.0040   -0.0592    0.0197 |      0.9955  
      LdrexHighR |  -0.5179    0.4531   -0.3658    0.0906 |      0.3845  
       LdrexLowR |  -0.2828    0.3486    0.1638    0.0244 |      0.7711  
      LdrexRebel |   0.2273    0.0218    0.2268   -0.7864 |      0.2780  
      LdrexDemEl |   0.2868   -0.0741    0.1040    0.3777 |      0.7588  
      LdrexParty |   0.5713   -0.1243   -0.2457    0.1467 |      0.5763  
      LdrexLoyal |   0.0612   -0.0208   -0.0299    0.0590 |      0.9914  
      LdrexReltv |  -0.0057    0.0457    0.1509    0.0222 |      0.9746  
     LdrexRulFam |  -0.3248   -0.7221    0.2596    0.0041 |      0.3056  
      LdrexOther |  -0.0561   -0.1606    0.0617    0.1364 |      0.9486  
    partye~mpers |   0.1276    0.4820    0.4131    0.1775 |      0.5493  
    partyexcom~n |   0.4035    0.0144   -0.2090   -0.2768 |      0.7167  
    partyexcom~e |   0.3031   -0.0222   -0.2137    0.0026 |      0.8620  
     createparty |  -0.2044    0.4862    0.3541    0.0285 |      0.5956  
    ---------------------------------------------------------------------

.                 rotate, oblique oblimin  

Factor analysis/correlation                      Number of obs    =      2,538
    Method: principal factors                    Retained factors =          4
    Rotation: oblique oblimin (Kaiser off)       Number of params =        330

    --------------------------------------------------------------------------
         Factor  |     Variance   Proportion    Rotated factors are correlated
    -------------+------------------------------------------------------------
        Factor1  |      9.32286       0.1333
        Factor2  |      8.75489       0.1251
        Factor3  |      6.71920       0.0960
        Factor4  |      5.40940       0.0773
    --------------------------------------------------------------------------
    LR test: independent vs. saturated: chi2(3486)=       . Prob>chi2 =      .

Rotated factor loadings (pattern matrix) and unique variances

    ---------------------------------------------------------------------
        Variable |  Factor1   Factor2   Factor3   Factor4 |   Uniqueness 
    -------------+----------------------------------------+--------------
    partyrbrstmp |   0.4903   -0.1125    0.3945   -0.2180 |      0.5971  
       militrank |  -0.0070   -0.8558   -0.0633   -0.1591 |      0.2277  
     ldrrotation |  -0.1114   -0.2910   -0.2236   -0.0606 |      0.8545  
      milconsult |  -0.2231   -0.5937   -0.2685   -0.0248 |      0.5317  
    milmerit_mil |  -0.0604    0.0606   -0.6075   -0.1986 |      0.5845  
    milmeritpers |   0.0788   -0.1113    0.5469    0.1672 |      0.6532  
      milnotrial |   0.2379   -0.2858    0.4727    0.2040 |      0.6068  
      plebiscite |   0.2781   -0.1070    0.1909   -0.3625 |      0.7678  
        heirclan |  -0.2032    0.1181    0.2956    0.3123 |      0.7674  
      officepers |   0.0458    0.0917    0.6816   -0.1377 |      0.5245  
     paramilpers |   0.0207    0.0314    0.5639   -0.0110 |      0.6856  
    ParamilParty |   0.3008    0.0618   -0.2366   -0.0339 |      0.8314  
     ParamilFReb |  -0.0890   -0.2005   -0.2078    0.2232 |      0.8753  
    supportparty |   0.9534    0.0448    0.0733    0.0733 |      0.0687  
     partyleader |   0.7033    0.1133    0.1077    0.1060 |      0.4525  
     localorgzns |   0.9138    0.0707    0.0154    0.0224 |      0.1435  
       partymins |   0.8043    0.0808   -0.0710    0.1012 |      0.2901  
       excomcivn |   0.7328    0.1652   -0.1147    0.0869 |      0.3564  
     multiethnic |   0.5663    0.1332   -0.2103   -0.2130 |      0.5545  
      monoethnic |   0.2932   -0.1089    0.3134    0.3160 |      0.7097  
       heirparty |   0.5198    0.2630   -0.4751   -0.0022 |      0.3387  
      heirfamily |  -0.3300    0.1610    0.6111    0.0862 |      0.4822  
      legcompetn |   0.3522    0.2906    0.0060   -0.3457 |      0.6852  
    leaderrela~s |  -0.1393    0.1964    0.5138    0.1203 |      0.6753  
       leaderciv |  -0.1047    0.8356   -0.1465   -0.3859 |      0.1586  
       leadermil |   0.0428   -0.9158    0.0976   -0.1469 |      0.1040  
     leaderrebel |   0.0975    0.0810    0.0799    0.8159 |      0.2894  
         heirciv |   0.1545    0.5740   -0.3692   -0.0661 |      0.4448  
          cabciv |   0.0608    0.5262    0.0623    0.0779 |      0.7023  
          cabmil |   0.0009   -0.6035   -0.0931   -0.0567 |      0.6294  
      partymilit |   0.3608    0.2217   -0.4012    0.2768 |      0.4973  
       ldrPriorD |   0.1357    0.3030    0.0477   -0.3813 |      0.7619  
        ldrParty |   0.2952    0.2551   -0.4378   -0.1153 |      0.5934  
          ldrMil |  -0.0808   -0.8385    0.0451   -0.1179 |      0.2362  
        ldrRebel |   0.0876    0.0948    0.0869    0.7566 |      0.3826  
          ldrCiv |   0.1030    0.0833    0.0163   -0.1545 |      0.9616  
        ldrOther |   0.0518    0.0447    0.2599   -0.0479 |      0.9301  
        ldrForgn |   0.1310    0.0164    0.0288    0.0538 |      0.9778  
        ldrHered |  -0.7174    0.4442    0.2114    0.0029 |      0.3056  
        SeizCoup |   0.0073   -0.7586    0.1232   -0.1260 |      0.3647  
       SeizRebel |   0.0218    0.1959   -0.2031    0.7134 |      0.3795  
       SeizUpris |  -0.0332   -0.0178   -0.0341   -0.0888 |      0.9890  
        SeizElec |   0.2033    0.3699   -0.0690   -0.4566 |      0.6247  
        SeizSucc |   0.0813   -0.0613    0.1356   -0.0578 |      0.9698  
         SeizFam |  -0.5161    0.3280    0.1556   -0.0229 |      0.6336  
     PartyhNoWin |   0.1834    0.0092    0.0845    0.0086 |      0.9612  
       PartyhWin |   0.0581   -0.0004    0.0603   -0.0510 |      0.9915  
       PartyhReb |   0.1670    0.0911   -0.3032    0.5926 |      0.4780  
    PartyhPrio~m |   0.3555    0.2791   -0.1261   -0.3580 |      0.6517  
    PartyhNopa~y |  -0.9534   -0.0448   -0.0733   -0.0733 |      0.0687  
      PartyhElec |   0.0215    0.0557    0.1039   -0.0523 |      0.9847  
    MilPartyAlly |   0.0918   -0.2068   -0.0113   -0.0321 |      0.9518  
      MilPartyNo |  -0.4697   -0.5443   -0.2911   -0.0336 |      0.3872  
    MilPartyPr~r |   0.2316   -0.2962   -0.0411   -0.1183 |      0.8588  
      nomilitary |  -0.0753    0.1615    0.0363   -0.0628 |      0.9669  
    milethnic_~e |   0.0144    0.0202   -0.3891   -0.1028 |      0.8356  
    milethnic~ro |  -0.0355    0.0714    0.1483   -0.0070 |      0.9732  
    milethnic~mo |   0.0537   -0.1754    0.3013    0.1646 |      0.8464  
    sectyapp_p~y |   0.1464    0.0808   -0.5294    0.2801 |      0.5787  
    sectyapppers |  -0.0124    0.1352    0.6749   -0.0514 |      0.5397  
    ElecldrPrD~t |  -0.0123    0.0643    0.0536   -0.0549 |      0.9909  
    ElecldrPrDem |  -0.0501    0.1279    0.0297   -0.1018 |      0.9727  
      ElecldrNot |  -0.1330   -0.4352   -0.3401    0.3716 |      0.5920  
       Elecldr1C |   0.3685   -0.0798    0.2260   -0.2220 |      0.7872  
       Elecldr1F |   0.1296    0.0495    0.0180    0.0435 |      0.9764  
     ElecldrMLeg |   0.1241    0.1477   -0.1008   -0.1063 |      0.9369  
    ElecldrMExec |   0.1887    0.1090    0.0466   -0.1338 |      0.9368  
    legnoms_in~t |  -0.0500    0.0221   -0.1292    0.3971 |      0.8257  
    legnoms_veto |   0.3590    0.1126    0.1153   -0.2917 |      0.7836  
    legnoms_no~o |  -0.0858    0.2208   -0.1427   -0.1506 |      0.9039  
    legnoms_pr~m |  -0.0246   -0.0310   -0.0488   -0.0253 |      0.9955  
      LdrexHighR |  -0.0418   -0.7538   -0.1156   -0.1557 |      0.3845  
       LdrexLowR |   0.0988   -0.3489    0.2983   -0.0361 |      0.7711  
      LdrexRebel |   0.0987    0.0939    0.1100    0.8174 |      0.2780  
      LdrexDemEl |   0.1951    0.3082    0.0259   -0.3514 |      0.7588  
      LdrexParty |   0.2692    0.3403   -0.4084   -0.1254 |      0.5763  
      LdrexLoyal |   0.0282    0.0464   -0.0460   -0.0569 |      0.9914  
      LdrexReltv |   0.0535    0.0245    0.1541   -0.0127 |      0.9746  
     LdrexRulFam |  -0.7174    0.4442    0.2114    0.0029 |      0.3056  
      LdrexOther |  -0.1315    0.1279    0.0539   -0.1337 |      0.9486  
    partye~mpers |   0.5219   -0.0967    0.4507   -0.1476 |      0.5493  
    partyexcom~n |   0.2141    0.1004   -0.3257    0.2885 |      0.7167  
    partyexcom~e |   0.1548    0.1063   -0.2923    0.0025 |      0.8620  
     createparty |   0.2795   -0.3312    0.4811   -0.0239 |      0.5956  
    ---------------------------------------------------------------------

Factor rotation matrix

    --------------------------------------------------
                 | Factor1  Factor2  Factor3  Factor4 
    -------------+------------------------------------
         Factor1 |  0.7538   0.6718  -0.3922   0.1627 
         Factor2 |  0.6475  -0.6643   0.1773   0.0008 
         Factor3 |  0.1020   0.3193   0.9020   0.1071 
         Factor4 |  0.0466   0.0744   0.0346  -0.9809 
    --------------------------------------------------

.                 predict pdpi1 pdpi2 pdpi3 pdpi4 
(regression scoring assumed)

Scoring coefficients (method = regression; based on oblimin(0) rotated factors)

    ------------------------------------------------------
        Variable |  Factor1   Factor2   Factor3   Factor4 
    -------------+----------------------------------------
    partyrbrstmp |  0.02427  -0.01692   0.02162  -0.02687 
       militrank |  0.03356  -0.04840  -0.08668  -0.04937 
     ldrrotation |  0.00490  -0.02154  -0.02974   0.00644 
      milconsult | -0.01717  -0.02426  -0.04679  -0.00531 
    milmerit_mil | -0.02008  -0.01096  -0.11755  -0.08540 
    milmeritpers |  0.00071  -0.01383   0.10230  -0.00100 
      milnotrial | -0.00228  -0.00002   0.02808   0.00462 
      plebiscite |  0.00442   0.02312   0.01241  -0.00086 
        heirclan |  0.00747   0.00745   0.01343   0.01007 
      officepers |  0.01971   0.00687   0.06576  -0.02611 
     paramilpers | -0.00631   0.00859   0.05132  -0.00944 
    ParamilParty |  0.01501   0.00161  -0.03891  -0.01689 
     ParamilFReb | -0.01481  -0.00945  -0.02932   0.00984 
    supportparty |  0.00000   0.00000   0.00000   0.00000 
     partyleader |  0.00587   0.01207   0.01478  -0.01901 
     localorgzns |  0.05994   0.01261   0.00234   0.03941 
       partymins |  0.02137   0.03456  -0.01647   0.03838 
       excomcivn |  0.03104  -0.01273  -0.01538  -0.01315 
     multiethnic |  0.08852   0.06168  -0.04994   0.06180 
      monoethnic |  0.06202   0.02623   0.04575   0.16986 
       heirparty |  0.05237   0.03977  -0.09345   0.02832 
      heirfamily | -0.01090  -0.00067   0.08415   0.00206 
      legcompetn |  0.02826  -0.00017  -0.01180  -0.06719 
    leaderrela~s | -0.00851   0.01582   0.04476   0.00545 
       leaderciv |  0.00000   0.00000   0.00000   0.00000 
       leadermil |  0.07961  -0.38701   0.07142   0.11222 
     leaderrebel |  0.00047  -0.05692   0.02926   0.24422 
         heirciv |  0.02778   0.02173  -0.05771  -0.01081 
          cabciv |  0.00597   0.04560   0.00071  -0.00608 
          cabmil |  0.00380  -0.09172  -0.03104  -0.02931 
      partymilit |  0.02164  -0.00071  -0.04273   0.03928 
       ldrPriorD |  0.05234   0.12471   0.02228  -0.07647 
        ldrParty |  0.08396   0.15887  -0.05946   0.00002 
          ldrMil |  0.00000   0.00000   0.00000   0.00000 
        ldrRebel |  0.07448   0.10663   0.04488   0.17668 
          ldrCiv |  0.01802   0.04552   0.00499  -0.03913 
        ldrOther |  0.02106   0.07401   0.05544   0.00695 
        ldrForgn |  0.05000   0.07453   0.00031   0.01964 
        ldrHered |  0.00000   0.00000   0.00000   0.00000 
        SeizCoup |  0.00114  -0.01236  -0.01369  -0.01149 
       SeizRebel |  0.01826  -0.01466  -0.08442   0.09961 
       SeizUpris |  0.01456   0.00513  -0.00886  -0.00778 
        SeizElec |  0.01671   0.03296  -0.00093  -0.04126 
        SeizSucc |  0.00686   0.01682   0.00503   0.01244 
         SeizFam | -0.00870   0.01028  -0.00753   0.00582 
     PartyhNoWin | -0.01200   0.00592   0.00370  -0.01076 
       PartyhWin | -0.02755   0.00128  -0.00460  -0.01805 
       PartyhReb | -0.03728   0.04763  -0.06980   0.07603 
    PartyhPrio~m | -0.01512   0.03364  -0.04627  -0.11407 
    PartyhNopa~y | -0.50566   0.04897  -0.08267  -0.01672 
      PartyhElec | -0.02503  -0.00067   0.01388  -0.00226 
    MilPartyAlly | -0.01740   0.02623   0.00991   0.01707 
      MilPartyNo | -0.04942   0.02602  -0.02012   0.01067 
    MilPartyPr~r | -0.02886   0.02831   0.01425  -0.00780 
      nomilitary | -0.01799   0.02415   0.02019  -0.01297 
    milethnic_~e |  0.00000   0.00000   0.00000   0.00000 
    milethnic~ro | -0.00828   0.01507   0.10973   0.05052 
    milethnic~mo |  0.00285  -0.01909   0.10391   0.06503 
    sectyapp_p~y |  0.01982   0.00585  -0.05440   0.05153 
    sectyapppers |  0.00626   0.01542   0.11158  -0.00395 
    ElecldrPrD~t | -0.00872   0.02032   0.03263  -0.02386 
    ElecldrPrDem | -0.00858   0.03430   0.02255  -0.04747 
      ElecldrNot |  0.00000   0.00000   0.00000   0.00000 
       Elecldr1C |  0.07435   0.02906   0.11478  -0.16334 
       Elecldr1F |  0.02726   0.03291   0.03910  -0.02894 
     ElecldrMLeg |  0.00936   0.05384   0.01066  -0.07518 
    ElecldrMExec |  0.03382   0.06705   0.07803  -0.13386 
    legnoms_in~t | -0.02127   0.01117  -0.00764   0.01124 
    legnoms_veto |  0.02360   0.02871   0.04383  -0.06005 
    legnoms_no~o | -0.03438   0.04311  -0.00892  -0.02508 
    legnoms_pr~m | -0.00044   0.00552  -0.00624   0.00001 
      LdrexHighR | -0.03167  -0.22924   0.03810  -0.11877 
       LdrexLowR |  0.01669  -0.10578   0.08025  -0.08005 
      LdrexRebel | -0.01498  -0.01542   0.09521   0.19336 
      LdrexDemEl | -0.00615   0.00123   0.03861  -0.06155 
      LdrexParty |  0.00000   0.00000   0.00000   0.00000 
      LdrexLoyal | -0.00896  -0.01347  -0.00255  -0.01174 
      LdrexReltv | -0.01561  -0.01017   0.05998   0.01252 
     LdrexRulFam | -0.20306   0.29486   0.21775  -0.00128 
      LdrexOther | -0.03864   0.01139   0.02448  -0.01610 
    partye~mpers |  0.05594   0.00413   0.13596  -0.04553 
    partyexcom~n | -0.00904   0.00847  -0.02736   0.03154 
    partyexcom~e | -0.01424   0.01711  -0.02153  -0.02098 
     createparty | -0.06975   0.06359   0.09919   0.00649 
    ------------------------------------------------------


.                 factor $allvar1 $allvar2 $allvar3 $allvar4 $allvar5 $allvar6 $allvar
> 7 $allvar8 $allvar9 $allvar10 dpi_parties dpi_party dpi_liec dpi_mil, factors(4)    
>         
(obs=2,537)
(collinear variables specified)

Factor analysis/correlation                      Number of obs    =      2,537
    Method: principal factors                    Retained factors =          4
    Rotation: (unrotated)                        Number of params =        346

    --------------------------------------------------------------------------
         Factor  |   Eigenvalue   Difference        Proportion   Cumulative
    -------------+------------------------------------------------------------
        Factor1  |     12.04419      4.41224            0.1632       0.1632
        Factor2  |      7.63195      1.64369            0.1034       0.2666
        Factor3  |      5.98826      0.35159            0.0811       0.3477
        Factor4  |      5.63667      2.14698            0.0764       0.4241
        Factor5  |      3.48969      0.50381            0.0473       0.4714
        Factor6  |      2.98588      0.51484            0.0405       0.5118
        Factor7  |      2.47104      0.10870            0.0335       0.5453
        Factor8  |      2.36234      0.25341            0.0320       0.5773
        Factor9  |      2.10892      0.18148            0.0286       0.6059
       Factor10  |      1.92744      0.00921            0.0261       0.6320
       Factor11  |      1.91823      0.20250            0.0260       0.6580
       Factor12  |      1.71573      0.19275            0.0232       0.6812
       Factor13  |      1.52297      0.05205            0.0206       0.7018
       Factor14  |      1.47092      0.09754            0.0199       0.7218
       Factor15  |      1.37339      0.08677            0.0186       0.7404
       Factor16  |      1.28661      0.07232            0.0174       0.7578
       Factor17  |      1.21429      0.08884            0.0165       0.7743
       Factor18  |      1.12544      0.02012            0.0152       0.7895
       Factor19  |      1.10533      0.04126            0.0150       0.8045
       Factor20  |      1.06406      0.01695            0.0144       0.8189
       Factor21  |      1.04711      0.05844            0.0142       0.8331
       Factor22  |      0.98867      0.01505            0.0134       0.8465
       Factor23  |      0.97362      0.06181            0.0132       0.8597
       Factor24  |      0.91181      0.04528            0.0124       0.8720
       Factor25  |      0.86653      0.08057            0.0117       0.8838
       Factor26  |      0.78596      0.03281            0.0106       0.8944
       Factor27  |      0.75315      0.03568            0.0102       0.9046
       Factor28  |      0.71747      0.01706            0.0097       0.9143
       Factor29  |      0.70041      0.06295            0.0095       0.9238
       Factor30  |      0.63746      0.04447            0.0086       0.9325
       Factor31  |      0.59299      0.04907            0.0080       0.9405
       Factor32  |      0.54392      0.02621            0.0074       0.9479
       Factor33  |      0.51772      0.02883            0.0070       0.9549
       Factor34  |      0.48889      0.01639            0.0066       0.9615
       Factor35  |      0.47249      0.02631            0.0064       0.9679
       Factor36  |      0.44618      0.03520            0.0060       0.9739
       Factor37  |      0.41097      0.08235            0.0056       0.9795
       Factor38  |      0.32862      0.00289            0.0045       0.9840
       Factor39  |      0.32573      0.01663            0.0044       0.9884
       Factor40  |      0.30910      0.05624            0.0042       0.9926
       Factor41  |      0.25285      0.00347            0.0034       0.9960
       Factor42  |      0.24939      0.02542            0.0034       0.9994
       Factor43  |      0.22397      0.01933            0.0030       1.0024
       Factor44  |      0.20464      0.02283            0.0028       1.0052
       Factor45  |      0.18181      0.01678            0.0025       1.0076
       Factor46  |      0.16502      0.02380            0.0022       1.0099
       Factor47  |      0.14122      0.00436            0.0019       1.0118
       Factor48  |      0.13687      0.02274            0.0019       1.0136
       Factor49  |      0.11413      0.00872            0.0015       1.0152
       Factor50  |      0.10541      0.02140            0.0014       1.0166
       Factor51  |      0.08401      0.01150            0.0011       1.0178
       Factor52  |      0.07252      0.01496            0.0010       1.0187
       Factor53  |      0.05755      0.00669            0.0008       1.0195
       Factor54  |      0.05087      0.00776            0.0007       1.0202
       Factor55  |      0.04310      0.00220            0.0006       1.0208
       Factor56  |      0.04091      0.01789            0.0006       1.0213
       Factor57  |      0.02301      0.01467            0.0003       1.0217
       Factor58  |      0.00835      0.00216            0.0001       1.0218
       Factor59  |      0.00619      0.00537            0.0001       1.0219
       Factor60  |      0.00082      0.00082            0.0000       1.0219
       Factor61  |      0.00000      0.00000            0.0000       1.0219
       Factor62  |      0.00000      0.00000            0.0000       1.0219
       Factor63  |      0.00000      0.00000            0.0000       1.0219
       Factor64  |      0.00000      0.00000            0.0000       1.0219
       Factor65  |     -0.00000      0.00000           -0.0000       1.0219
       Factor66  |     -0.00000      0.00000           -0.0000       1.0219
       Factor67  |     -0.00000      0.00141           -0.0000       1.0219
       Factor68  |     -0.00141      0.00208           -0.0000       1.0218
       Factor69  |     -0.00349      0.00694           -0.0000       1.0218
       Factor70  |     -0.01043      0.00181           -0.0001       1.0217
       Factor71  |     -0.01223      0.01281           -0.0002       1.0215
       Factor72  |     -0.02504      0.00591           -0.0003       1.0212
       Factor73  |     -0.03095      0.00343           -0.0004       1.0207
       Factor74  |     -0.03438      0.02002           -0.0005       1.0203
       Factor75  |     -0.05440      0.00285           -0.0007       1.0195
       Factor76  |     -0.05724      0.00813           -0.0008       1.0188
       Factor77  |     -0.06537      0.01877           -0.0009       1.0179
       Factor78  |     -0.08414      0.00555           -0.0011       1.0167
       Factor79  |     -0.08969      0.00545           -0.0012       1.0155
       Factor80  |     -0.09515      0.00831           -0.0013       1.0142
       Factor81  |     -0.10346      0.00765           -0.0014       1.0128
       Factor82  |     -0.11111      0.00301           -0.0015       1.0113
       Factor83  |     -0.11412      0.00472           -0.0015       1.0098
       Factor84  |     -0.11884      0.00566           -0.0016       1.0082
       Factor85  |     -0.12449      0.01319           -0.0017       1.0065
       Factor86  |     -0.13768      0.01354           -0.0019       1.0046
       Factor87  |     -0.15123      0.03801           -0.0020       1.0026
       Factor88  |     -0.18924            .           -0.0026       1.0000
    --------------------------------------------------------------------------
    LR test: independent vs. saturated: chi2(3828)= 8.1e+05 Prob>chi2 = 0.0000

Factor loadings (pattern matrix) and unique variances

    ---------------------------------------------------------------------
        Variable |  Factor1   Factor2   Factor3   Factor4 |   Uniqueness 
    -------------+----------------------------------------+--------------
    partyrbrstmp |   0.0998    0.4665    0.3577   -0.0188 |      0.6441  
       militrank |  -0.5922    0.5416   -0.2521   -0.2537 |      0.2282  
     ldrrotation |  -0.1808    0.0758   -0.2357   -0.2026 |      0.8650  
      milconsult |  -0.4895    0.1737   -0.4121   -0.1608 |      0.5346  
    milmerit_mil |   0.2092   -0.1703   -0.4507   -0.3802 |      0.5796  
    milmeritpers |  -0.2090    0.2063    0.3958    0.3258 |      0.6509  
      milnotrial |  -0.1885    0.4118    0.2769    0.3379 |      0.6040  
      plebiscite |   0.0236    0.3081    0.2337   -0.2663 |      0.7790  
        heirclan |  -0.1394   -0.1719    0.2126    0.3564 |      0.7788  
      officepers |  -0.1656    0.0970    0.6482    0.0866 |      0.5355  
     paramilpers |  -0.1616    0.0982    0.5133    0.1373 |      0.6820  
    ParamilParty |   0.3195    0.1117   -0.2019   -0.0143 |      0.8445  
     ParamilFReb |  -0.0928    0.0197   -0.2797    0.0988 |      0.9030  
    supportparty |   0.7030    0.6212    0.0880    0.1836 |      0.0785  
     partyleader |   0.5467    0.4060    0.1034    0.2347 |      0.4704  
     localorgzns |   0.7137    0.5750    0.0635    0.1074 |      0.1445  
       partymins |   0.6684    0.4718   -0.0494    0.1753 |      0.2974  
       excomcivn |   0.6977    0.3619   -0.0506    0.1315 |      0.3625  
     multiethnic |   0.5632    0.2727   -0.0727   -0.1955 |      0.5649  
      monoethnic |   0.0489    0.3017    0.1701    0.4056 |      0.7132  
       heirparty |   0.7301    0.0956   -0.3294   -0.0512 |      0.3468  
      heirfamily |  -0.3465   -0.2193    0.5425    0.2392 |      0.4804  
      legcompetn |   0.5123    0.1136    0.2914   -0.4224 |      0.4613  
    leaderrela~s |  -0.1311   -0.1276    0.4790    0.2465 |      0.6763  
       leaderciv |   0.5294   -0.6207    0.1960   -0.3259 |      0.1899  
       leadermil |  -0.6641    0.6416   -0.1397   -0.1842 |      0.0939  
     leaderrebel |   0.1773   -0.0024   -0.0936    0.7800 |      0.3514  
         heirciv |   0.6331   -0.3308   -0.1493   -0.0812 |      0.4608  
          cabciv |   0.4151   -0.2696    0.2143    0.0972 |      0.6996  
          cabmil |  -0.4049    0.3582   -0.2580   -0.0995 |      0.6313  
      partymilit |   0.5680    0.0040   -0.3711    0.2435 |      0.4804  
       ldrPriorD |   0.2357   -0.0951    0.1882   -0.2446 |      0.8402  
        ldrParty |   0.5517   -0.0282   -0.2539   -0.2040 |      0.5887  
          ldrMil |  -0.6724    0.4961   -0.1708   -0.1822 |      0.2395  
        ldrRebel |   0.1813   -0.0111   -0.0599    0.7093 |      0.4604  
          ldrCiv |   0.1147    0.0196    0.0770   -0.1178 |      0.9667  
        ldrOther |  -0.0285    0.0450    0.2510    0.0410 |      0.9325  
        ldrForgn |   0.0872    0.0746   -0.0003    0.1057 |      0.9757  
        ldrHered |  -0.2872   -0.7216    0.2887    0.0298 |      0.3125  
        SeizCoup |  -0.5903    0.5187   -0.0840   -0.1400 |      0.3559  
       SeizRebel |   0.2921   -0.1735   -0.2871    0.6099 |      0.4301  
       SeizUpris |  -0.0285   -0.0092   -0.0069   -0.1070 |      0.9876  
        SeizElec |   0.3743   -0.1035    0.1387   -0.3552 |      0.7038  
        SeizSucc |  -0.0213    0.1202    0.1335   -0.0358 |      0.9660  
         SeizFam |  -0.1990   -0.5190    0.2260   -0.0096 |      0.6399  
     PartyhNoWin |   0.1070    0.1240    0.0705    0.0674 |      0.9637  
       PartyhWin |   0.0288    0.0508    0.0814   -0.0460 |      0.9879  
       PartyhReb |   0.3494   -0.0180   -0.3694    0.4744 |      0.5161  
    PartyhPrio~m |   0.4476    0.0432    0.0386   -0.2646 |      0.7263  
    PartyhNopa~y |  -0.7030   -0.6212   -0.0880   -0.1836 |      0.0785  
      PartyhElec |   0.0245   -0.0026    0.1319   -0.0293 |      0.9811  
    MilPartyAlly |  -0.0823    0.1978   -0.0602   -0.0411 |      0.9488  
      MilPartyNo |  -0.6283   -0.0245   -0.4268   -0.1867 |      0.3876  
    MilPartyPr~r |  -0.0278    0.3541   -0.0679   -0.1504 |      0.8466  
      nomilitary |   0.0323   -0.1477    0.0814   -0.0281 |      0.9697  
    milethnic_~e |   0.1671   -0.0610   -0.2923   -0.2217 |      0.8338  
    milethnic~ro |  -0.0375   -0.0450    0.1390    0.0534 |      0.9744  
    milethnic~mo |  -0.1800    0.1894    0.1716    0.2288 |      0.8499  
    sectyapp_p~y |   0.3734   -0.0577   -0.5076    0.1412 |      0.5797  
    sectyapppers |  -0.1606    0.0274    0.6420    0.1468 |      0.5397  
    ElecldrPrD~t |   0.0188   -0.0369    0.0843   -0.0402 |      0.9896  
    ElecldrPrDem |   0.0363   -0.1052    0.0868   -0.0819 |      0.9734  
      ElecldrNot |  -0.2810    0.0819   -0.5674    0.2806 |      0.5137  
       Elecldr1C |   0.0978    0.3370    0.2203   -0.0819 |      0.8216  
       Elecldr1F |   0.1186    0.0583    0.0128    0.0784 |      0.9762  
     ElecldrMLeg |   0.2401   -0.0119    0.0106   -0.1445 |      0.9212  
    ElecldrMExec |   0.2314    0.1060    0.1806   -0.1952 |      0.8645  
    legnoms_in~t |   0.0409   -0.1009   -0.2332    0.3710 |      0.7961  
    legnoms_veto |   0.3165    0.2252    0.2747   -0.2830 |      0.6935  
    legnoms_no~o |   0.1661   -0.1936    0.0178   -0.2389 |      0.8776  
    legnoms_pr~m |  -0.0153    0.0029   -0.0367   -0.0560 |      0.9953  
      LdrexHighR |  -0.5248    0.4438   -0.2686   -0.2620 |      0.3868  
       LdrexLowR |  -0.2967    0.3478    0.1602    0.0464 |      0.7632  
      LdrexRebel |   0.1823   -0.0019   -0.0596    0.7814 |      0.3526  
      LdrexDemEl |   0.2915   -0.0628    0.1636   -0.2147 |      0.8382  
      LdrexParty |   0.5770   -0.1077   -0.2145   -0.1797 |      0.5771  
      LdrexLoyal |   0.0790   -0.0116    0.0084   -0.0913 |      0.9852  
      LdrexReltv |  -0.0033    0.0394    0.1423    0.0443 |      0.9762  
     LdrexRulFam |  -0.2872   -0.7216    0.2887    0.0298 |      0.3125  
      LdrexOther |  -0.0252   -0.1537    0.1262   -0.1306 |      0.9427  
    partye~mpers |   0.1210    0.4857    0.3949    0.0664 |      0.5891  
    partyexcom~n |   0.3721    0.0124   -0.3167    0.2007 |      0.7208  
    partyexcom~e |   0.3009   -0.0029   -0.2018   -0.0848 |      0.8616  
     createparty |  -0.2016    0.4879    0.3448    0.1016 |      0.5921  
     dpi_parties |   0.5391    0.1688    0.2659   -0.4060 |      0.4454  
       dpi_party |   0.6208    0.1889    0.2482   -0.2487 |      0.4555  
        dpi_liec |   0.5119    0.1470    0.2875   -0.4127 |      0.4634  
    dpi_military |  -0.5553    0.6255   -0.0473    0.0097 |      0.2980  
    ---------------------------------------------------------------------

.                 rotate, promax(3) 

Factor analysis/correlation                      Number of obs    =      2,537
    Method: principal factors                    Retained factors =          4
    Rotation: oblique promax (Kaiser off)        Number of params =        346

    --------------------------------------------------------------------------
         Factor  |     Variance   Proportion    Rotated factors are correlated
    -------------+------------------------------------------------------------
        Factor1  |     10.26387       0.1391
        Factor2  |      9.95638       0.1349
        Factor3  |      6.72096       0.0911
        Factor4  |      5.85856       0.0794
    --------------------------------------------------------------------------
    LR test: independent vs. saturated: chi2(3828)= 8.1e+05 Prob>chi2 = 0.0000

Rotated factor loadings (pattern matrix) and unique variances

    ---------------------------------------------------------------------
        Variable |  Factor1   Factor2   Factor3   Factor4 |   Uniqueness 
    -------------+----------------------------------------+--------------
    partyrbrstmp |   0.4418    0.1938    0.3603    0.1502 |      0.6441  
       militrank |   0.0076    0.8906   -0.0964    0.1966 |      0.2282  
     ldrrotation |  -0.0949    0.2762   -0.2264    0.1287 |      0.8650  
      milconsult |  -0.2094    0.5800   -0.2875    0.0396 |      0.5346  
    milmerit_mil |  -0.0809   -0.0378   -0.6062    0.2239 |      0.5796  
    milmeritpers |   0.0990    0.0935    0.5434   -0.1860 |      0.6509  
      milnotrial |   0.2750    0.2790    0.4610   -0.2228 |      0.6040  
      plebiscite |   0.2212    0.1984    0.1656    0.3442 |      0.7790  
        heirclan |  -0.1585   -0.1875    0.3179   -0.2902 |      0.7788  
      officepers |   0.0046   -0.0565    0.6741    0.1162 |      0.5355  
     paramilpers |   0.0133   -0.0228    0.5657    0.0265 |      0.6820  
    ParamilParty |   0.2835   -0.0275   -0.2513   -0.0407 |      0.8445  
     ParamilFReb |  -0.0333    0.1423   -0.2002   -0.1791 |      0.9030  
    supportparty |   0.9745    0.0205    0.0530   -0.1180 |      0.0785  
     partyleader |   0.7131   -0.0721    0.0919   -0.1742 |      0.4704  
     localorgzns |   0.9306   -0.0001   -0.0024   -0.0540 |      0.1445  
       partymins |   0.8284   -0.0299   -0.0873   -0.1592 |      0.2974  
       excomcivn |   0.7515   -0.1244   -0.1239   -0.1227 |      0.3625  
     multiethnic |   0.5384   -0.0513   -0.2272    0.1825 |      0.5649  
      monoethnic |   0.3460    0.0796    0.3126   -0.3258 |      0.7132  
       heirparty |   0.5206   -0.2273   -0.4806   -0.0435 |      0.3468  
      heirfamily |  -0.3388   -0.1927    0.6265   -0.0801 |      0.4804  
      legcompetn |   0.3497   -0.2248    0.0257    0.5036 |      0.4613  
    leaderrela~s |  -0.1298   -0.2251    0.5312   -0.1012 |      0.6763  
       leaderciv |  -0.2117   -0.7945   -0.1336    0.3435 |      0.1899  
       leadermil |   0.0569    0.9609    0.0592    0.1682 |      0.0939  
     leaderrebel |   0.2420   -0.2124    0.1178   -0.7833 |      0.3514  
         heirciv |   0.1184   -0.5584   -0.3589    0.0177 |      0.4608  
          cabciv |   0.0697   -0.5362    0.0913   -0.0428 |      0.6996  
          cabmil |   0.0060    0.6143   -0.1226    0.0363 |      0.6313  
      partymilit |   0.3946   -0.2449   -0.3989   -0.3465 |      0.4804  
       ldrPriorD |   0.0360   -0.2273    0.0258    0.2890 |      0.8402  
        ldrParty |   0.2846   -0.2191   -0.4334    0.1206 |      0.5887  
          ldrMil |  -0.0650    0.8617    0.0139    0.1494 |      0.2395  
        ldrRebel |   0.2263   -0.2199    0.1242   -0.7052 |      0.4604  
          ldrCiv |   0.0699   -0.0546    0.0084    0.1385 |      0.9667  
        ldrOther |   0.0315   -0.0379    0.2558    0.0382 |      0.9325  
        ldrForgn |   0.1329   -0.0105    0.0222   -0.0985 |      0.9757  
        ldrHered |  -0.7451   -0.4988    0.2464    0.0204 |      0.3125  
        SeizCoup |   0.0145    0.7964    0.0901    0.1360 |      0.3559  
       SeizRebel |   0.1436   -0.3184   -0.1637   -0.6856 |      0.4301  
       SeizUpris |  -0.0439    0.0305   -0.0345    0.1010 |      0.9876  
        SeizElec |   0.0966   -0.2774   -0.0889    0.3810 |      0.7038  
        SeizSucc |   0.0799    0.0689    0.1323    0.0810 |      0.9660  
         SeizFam |  -0.5359   -0.3635    0.1824    0.0502 |      0.6399  
     PartyhNoWin |   0.1801    0.0004    0.0768   -0.0375 |      0.9637  
       PartyhWin |   0.0530    0.0048    0.0599    0.0718 |      0.9879  
       PartyhReb |   0.2785   -0.1786   -0.2749   -0.5710 |      0.5161  
    PartyhPrio~m |   0.2731   -0.1883   -0.1499    0.2708 |      0.7263  
    PartyhNopa~y |  -0.9745   -0.0205   -0.0530    0.1180 |      0.0785  
      PartyhElec |   0.0118   -0.0540    0.1054    0.0681 |      0.9811  
    MilPartyAlly |   0.0972    0.2288   -0.0219    0.0317 |      0.9488  
      MilPartyNo |  -0.4603    0.5142   -0.3024    0.0498 |      0.3876  
    MilPartyPr~r |   0.2375    0.3417   -0.0556    0.1434 |      0.8466  
      nomilitary |  -0.1002   -0.1556    0.0382    0.0442 |      0.9697  
    milethnic_~e |   0.0111   -0.0083   -0.3862    0.1237 |      0.8338  
    milethnic~ro |  -0.0468   -0.0686    0.1472   -0.0122 |      0.9744  
    milethnic~mo |   0.0813    0.1543    0.2982   -0.1604 |      0.8499  
    sectyapp_p~y |   0.2010   -0.1187   -0.5179   -0.2920 |      0.5797  
    sectyapppers |  -0.0376   -0.1225    0.6769    0.0525 |      0.5397  
    ElecldrPrD~t |  -0.0223   -0.0600    0.0551    0.0624 |      0.9896  
    ElecldrPrDem |  -0.0729   -0.1168    0.0309    0.1001 |      0.9734  
      ElecldrNot |  -0.0789    0.3605   -0.3527   -0.4389 |      0.5137  
       Elecldr1C |   0.3227    0.1499    0.1977    0.1632 |      0.8216  
       Elecldr1F |   0.1354   -0.0406    0.0158   -0.0688 |      0.9762  
     ElecldrMLeg |   0.1178   -0.1246   -0.0952    0.1429 |      0.9212  
    ElecldrMExec |   0.2021   -0.0740    0.0624    0.2493 |      0.8645  
    legnoms_in~t |   0.0031   -0.0924   -0.1197   -0.4347 |      0.7961  
    legnoms_veto |   0.3385   -0.0448    0.1164    0.3690 |      0.6935  
    legnoms_no~o |  -0.0902   -0.2097   -0.1250    0.2270 |      0.8776  
    legnoms_pr~m |  -0.0180    0.0331   -0.0473    0.0433 |      0.9953  
      LdrexHighR |  -0.0297    0.7825   -0.1435    0.1948 |      0.3868  
       LdrexLowR |   0.1022    0.3813    0.2793    0.0208 |      0.7632  
      LdrexRebel |   0.2467   -0.2263    0.1482   -0.7745 |      0.3526  
      LdrexDemEl |   0.1016   -0.2314    0.0036    0.2545 |      0.8382  
      LdrexParty |   0.2424   -0.3130   -0.4064    0.1048 |      0.5771  
      LdrexLoyal |   0.0252   -0.0408   -0.0417    0.0905 |      0.9852  
      LdrexReltv |   0.0407   -0.0218    0.1504    0.0020 |      0.9762  
     LdrexRulFam |  -0.7451   -0.4988    0.2464    0.0204 |      0.3125  
      LdrexOther |  -0.1578   -0.1236    0.0599    0.1566 |      0.9427  
    partye~mpers |   0.4861    0.1692    0.4189    0.0799 |      0.5891  
    partyexcom~n |   0.2715   -0.1361   -0.3145   -0.2885 |      0.7208  
    partyexcom~e |   0.1681   -0.0938   -0.2844    0.0217 |      0.8616  
     createparty |   0.2885    0.3661    0.4618    0.0304 |      0.5921  
     dpi_parties |   0.4128   -0.1917    0.0084    0.4829 |      0.4454  
       dpi_party |   0.5072   -0.2451    0.0250    0.3264 |      0.4555  
        dpi_liec |   0.3776   -0.1990    0.0297    0.4947 |      0.4634  
    dpi_military |   0.1486    0.8211    0.1769    0.0076 |      0.2980  
    ---------------------------------------------------------------------

Factor rotation matrix

    --------------------------------------------------
                 | Factor1  Factor2  Factor3  Factor4 
    -------------+------------------------------------
         Factor1 |  0.7755  -0.7446  -0.3417   0.1638 
         Factor2 |  0.6217   0.6098   0.1810   0.0570 
         Factor3 |  0.0908  -0.2665   0.8780   0.3519 
         Factor4 |  0.0628  -0.0518   0.2822  -0.9198 
    --------------------------------------------------

.                 predict dpi1 dpi2 dpi3 dpi4     
(regression scoring assumed)

Scoring coefficients (method = regression; based on promax(3) rotated factors)

    ------------------------------------------------------
        Variable |  Factor1   Factor2   Factor3   Factor4 
    -------------+----------------------------------------
    partyrbrstmp |  0.02122   0.01285   0.02224   0.02246 
       militrank |  0.02720   0.04719  -0.08794   0.03606 
     ldrrotation |  0.00071   0.00893  -0.03150  -0.00722 
      milconsult | -0.01615   0.02351  -0.04648   0.00568 
    milmerit_mil | -0.02116   0.01108  -0.11619   0.07164 
    milmeritpers |  0.00205   0.01615   0.10164  -0.00115 
      milnotrial | -0.00185   0.00316   0.02812  -0.00392 
      plebiscite |  0.00716  -0.01945   0.01246   0.00642 
        heirclan |  0.00981  -0.00342   0.01501  -0.00467 
      officepers |  0.01977  -0.00072   0.06540   0.02783 
     paramilpers | -0.00317  -0.00761   0.05189   0.01671 
    ParamilParty |  0.01367  -0.00147  -0.03949   0.01769 
     ParamilFReb | -0.01427   0.00253  -0.02882  -0.00851 
    supportparty |  0.48395   0.10428   0.08953   0.02878 
     partyleader |  0.00702  -0.01348   0.01490   0.02315 
     localorgzns |  0.05683   0.00002   0.00400  -0.03955 
       partymins |  0.02548  -0.04517  -0.01897  -0.02653 
       excomcivn |  0.02480   0.00946  -0.01612   0.00776 
     multiethnic |  0.06123  -0.12249  -0.05408  -0.06584 
      monoethnic |  0.04172  -0.08457   0.04243  -0.16109 
       heirparty |  0.04170  -0.05031  -0.09672  -0.03219 
      heirfamily | -0.00871   0.00523   0.08326   0.00052 
      legcompetn |  0.04379  -0.01667  -0.00474   0.10901 
    leaderrela~s | -0.00730  -0.01150   0.04537  -0.00512 
       leaderciv |  0.00000   0.00000   0.00000   0.00000 
       leadermil |  0.06551   0.36622   0.07158  -0.11490 
     leaderrebel |  0.00577   0.04529   0.03263  -0.20543 
         heirciv |  0.02740  -0.02084  -0.05571   0.01353 
          cabciv |  0.00856  -0.03565   0.00348   0.01176 
          cabmil | -0.00388   0.09046  -0.03416   0.00692 
      partymilit |  0.01713  -0.00345  -0.04400  -0.03839 
       ldrPriorD |  0.04472  -0.10991   0.01783   0.06374 
        ldrParty |  0.07995  -0.13478  -0.05786   0.01313 
          ldrMil |  0.00000   0.00000   0.00000   0.00000 
        ldrRebel |  0.07813  -0.09810   0.05036  -0.14439 
          ldrCiv |  0.01511  -0.03953   0.00338   0.03331 
        ldrOther |  0.02145  -0.07072   0.05490   0.00291 
        ldrForgn |  0.04686  -0.05863  -0.00056  -0.02051 
        ldrHered |  0.00000   0.00000   0.00000   0.00000 
        SeizCoup |  0.00066   0.03659  -0.01367  -0.00465 
       SeizRebel |  0.01734   0.00829  -0.08168  -0.08781 
       SeizUpris |  0.01416   0.00071  -0.00843   0.00647 
        SeizElec |  0.01299  -0.02854  -0.00246   0.03640 
        SeizSucc |  0.00730  -0.01282   0.00557  -0.01134 
         SeizFam | -0.00697  -0.00955  -0.00621  -0.00240 
     PartyhNoWin | -0.01133  -0.00306   0.00333   0.00616 
       PartyhWin | -0.02496  -0.00486  -0.00435   0.01951 
       PartyhReb | -0.03224  -0.03708  -0.06778  -0.06884 
    PartyhPrio~m | -0.01878  -0.01250  -0.04880   0.07807 
    PartyhNopa~y |  0.00000   0.00000   0.00000   0.00000 
      PartyhElec | -0.02332  -0.00557   0.01297   0.00192 
    MilPartyAlly | -0.01529  -0.03035   0.01008  -0.01488 
      MilPartyNo | -0.04412  -0.02125  -0.02083  -0.01366 
    MilPartyPr~r | -0.02231  -0.02312   0.01618   0.01007 
      nomilitary | -0.01647  -0.02044   0.01975   0.00927 
    milethnic_~e |  0.00000   0.00000   0.00000   0.00000 
    milethnic~ro | -0.00589  -0.01112   0.10849  -0.04953 
    milethnic~mo |  0.00367   0.01835   0.10297  -0.06007 
    sectyapp_p~y |  0.01981  -0.00033  -0.05326  -0.04860 
    sectyapppers |  0.00771  -0.01239   0.11025   0.00648 
    ElecldrPrD~t | -0.00639  -0.01846   0.03318   0.02538 
    ElecldrPrDem | -0.00464  -0.02440   0.02443   0.04524 
      ElecldrNot |  0.00000   0.00000   0.00000   0.00000 
       Elecldr1C |  0.07097  -0.01441   0.11541   0.16089 
       Elecldr1F |  0.02875  -0.01889   0.03998   0.03199 
     ElecldrMLeg |  0.01428  -0.03889   0.01583   0.08211 
    ElecldrMExec |  0.04358  -0.04065   0.08788   0.15161 
    legnoms_in~t | -0.01704  -0.00906  -0.00673  -0.01306 
    legnoms_veto |  0.02270  -0.01619   0.04687   0.04901 
    legnoms_no~o | -0.03074  -0.03242  -0.00656   0.01299 
    legnoms_pr~m | -0.00099  -0.00172  -0.00636  -0.00424 
      LdrexHighR | -0.03284   0.24663   0.03362   0.09807 
       LdrexLowR |  0.01632   0.13427   0.07701   0.06376 
      LdrexRebel | -0.00519   0.02784   0.09847  -0.17332 
      LdrexDemEl | -0.00883   0.01194   0.03576   0.04075 
      LdrexParty |  0.00000   0.00000   0.00000   0.00000 
      LdrexLoyal | -0.00755   0.01189  -0.00171   0.01672 
      LdrexReltv | -0.01542   0.01404   0.05883  -0.01314 
     LdrexRulFam | -0.16753  -0.24560   0.22412   0.02717 
      LdrexOther | -0.03440  -0.00510   0.02531   0.01694 
    partye~mpers |  0.05309   0.01082   0.13101   0.04245 
    partyexcom~n | -0.00464  -0.00323  -0.02884  -0.02393 
    partyexcom~e | -0.00939  -0.00718  -0.02231   0.02706 
     createparty | -0.06356  -0.05851   0.09676  -0.00837 
     dpi_parties |  0.07851  -0.06634   0.01970   0.15108 
       dpi_party |  0.04977  -0.00832   0.01753   0.01146 
        dpi_liec |  0.01587  -0.01810  -0.00976   0.10810 
    dpi_military | -0.00663   0.00188   0.01788  -0.01313 
    ------------------------------------------------------


.                 pwcorr pr1 pr2 pr3 pr4 pdpi1 pdpi2 pdpi3 pdpi4

             |      pr1      pr2      pr3      pr4    pdpi1    pdpi2    pdpi3
-------------+---------------------------------------------------------------
         pr1 |   1.0000 
         pr2 |  -0.1649   1.0000 
         pr3 |  -0.1033   0.0773   1.0000 
         pr4 |  -0.0596   0.0733  -0.0525   1.0000 
       pdpi1 |   0.9957  -0.1214  -0.1199  -0.0017   1.0000 
       pdpi2 |   0.1141  -0.9921  -0.0342  -0.0681   0.0662   1.0000 
       pdpi3 |  -0.1401   0.0623   0.9946  -0.0122  -0.1548  -0.0146   1.0000 
       pdpi4 |   0.0351  -0.0540  -0.0431   0.9770   0.0877   0.0517  -0.0053 

             |    pdpi4
-------------+---------
       pdpi4 |   1.0000 

.                 pwcorr pr1 dpi1

             |      pr1     dpi1
-------------+------------------
         pr1 |   1.0000 
        dpi1 |   0.9879   1.0000 

.                 pwcorr pr2 dpi2

             |      pr2     dpi2
-------------+------------------
         pr2 |   1.0000 
        dpi2 |   0.9943   1.0000 

.                 drop pdpi* dpi1 dpi2 dpi3 dpi4

.                 * Weeks comparison Regime Types *
.                 factor $allvar1 $allvar2 $allvar3 $allvar4 $allvar5 $allvar6 $allvar
> 7 $allvar8 $allvar9 $allvar10 if Junta~=., factors(4)
(obs=3,896)
(collinear variables specified)

Factor analysis/correlation                      Number of obs    =      3,896
    Method: principal factors                    Retained factors =          4
    Rotation: (unrotated)                        Number of params =        330

    --------------------------------------------------------------------------
         Factor  |   Eigenvalue   Difference        Proportion   Cumulative
    -------------+------------------------------------------------------------
        Factor1  |     11.42569      4.28079            0.1673       0.1673
        Factor2  |      7.14490      1.59507            0.1046       0.2720
        Factor3  |      5.54982      0.67194            0.0813       0.3533
        Factor4  |      4.87788      1.76365            0.0714       0.4247
        Factor5  |      3.11423      0.47463            0.0456       0.4703
        Factor6  |      2.63960      0.14925            0.0387       0.5090
        Factor7  |      2.49035      0.37826            0.0365       0.5455
        Factor8  |      2.11209      0.23214            0.0309       0.5764
        Factor9  |      1.87995      0.04948            0.0275       0.6039
       Factor10  |      1.83047      0.23274            0.0268       0.6307
       Factor11  |      1.59773      0.06664            0.0234       0.6541
       Factor12  |      1.53108      0.03994            0.0224       0.6766
       Factor13  |      1.49114      0.05338            0.0218       0.6984
       Factor14  |      1.43776      0.10807            0.0211       0.7195
       Factor15  |      1.32970      0.05210            0.0195       0.7389
       Factor16  |      1.27759      0.06360            0.0187       0.7577
       Factor17  |      1.21399      0.07358            0.0178       0.7754
       Factor18  |      1.14041      0.04384            0.0167       0.7921
       Factor19  |      1.09657      0.03965            0.0161       0.8082
       Factor20  |      1.05693      0.05435            0.0155       0.8237
       Factor21  |      1.00257      0.08232            0.0147       0.8384
       Factor22  |      0.92025      0.00394            0.0135       0.8518
       Factor23  |      0.91631      0.08001            0.0134       0.8653
       Factor24  |      0.83630      0.05571            0.0122       0.8775
       Factor25  |      0.78059      0.03163            0.0114       0.8889
       Factor26  |      0.74896      0.02604            0.0110       0.8999
       Factor27  |      0.72293      0.05562            0.0106       0.9105
       Factor28  |      0.66731      0.02251            0.0098       0.9203
       Factor29  |      0.64480      0.03444            0.0094       0.9297
       Factor30  |      0.61036      0.04499            0.0089       0.9387
       Factor31  |      0.56537      0.02836            0.0083       0.9469
       Factor32  |      0.53701      0.04779            0.0079       0.9548
       Factor33  |      0.48922      0.03919            0.0072       0.9620
       Factor34  |      0.45003      0.02191            0.0066       0.9686
       Factor35  |      0.42812      0.05393            0.0063       0.9748
       Factor36  |      0.37419      0.01438            0.0055       0.9803
       Factor37  |      0.35982      0.01977            0.0053       0.9856
       Factor38  |      0.34005      0.01952            0.0050       0.9906
       Factor39  |      0.32053      0.03711            0.0047       0.9953
       Factor40  |      0.28342      0.02092            0.0042       0.9994
       Factor41  |      0.26250      0.02849            0.0038       1.0033
       Factor42  |      0.23401      0.02996            0.0034       1.0067
       Factor43  |      0.20405      0.02448            0.0030       1.0097
       Factor44  |      0.17957      0.02311            0.0026       1.0123
       Factor45  |      0.15646      0.01595            0.0023       1.0146
       Factor46  |      0.14051      0.01339            0.0021       1.0166
       Factor47  |      0.12712      0.02406            0.0019       1.0185
       Factor48  |      0.10306      0.00595            0.0015       1.0200
       Factor49  |      0.09711      0.02329            0.0014       1.0214
       Factor50  |      0.07383      0.00868            0.0011       1.0225
       Factor51  |      0.06514      0.01465            0.0010       1.0235
       Factor52  |      0.05049      0.01174            0.0007       1.0242
       Factor53  |      0.03876      0.00873            0.0006       1.0248
       Factor54  |      0.03002      0.01115            0.0004       1.0252
       Factor55  |      0.01887      0.00326            0.0003       1.0255
       Factor56  |      0.01561      0.00689            0.0002       1.0257
       Factor57  |      0.00871      0.00695            0.0001       1.0259
       Factor58  |      0.00176      0.00176            0.0000       1.0259
       Factor59  |      0.00000      0.00000            0.0000       1.0259
       Factor60  |      0.00000      0.00000            0.0000       1.0259
       Factor61  |      0.00000      0.00000            0.0000       1.0259
       Factor62  |      0.00000      0.00000            0.0000       1.0259
       Factor63  |     -0.00000      0.00117           -0.0000       1.0259
       Factor64  |     -0.00117      0.00492           -0.0000       1.0259
       Factor65  |     -0.00608      0.00764           -0.0001       1.0258
       Factor66  |     -0.01372      0.00342           -0.0002       1.0256
       Factor67  |     -0.01714      0.00398           -0.0003       1.0253
       Factor68  |     -0.02112      0.00955           -0.0003       1.0250
       Factor69  |     -0.03066      0.00939           -0.0004       1.0246
       Factor70  |     -0.04006      0.01949           -0.0006       1.0240
       Factor71  |     -0.05955      0.00814           -0.0009       1.0231
       Factor72  |     -0.06769      0.01111           -0.0010       1.0221
       Factor73  |     -0.07880      0.00269           -0.0012       1.0210
       Factor74  |     -0.08148      0.00664           -0.0012       1.0198
       Factor75  |     -0.08812      0.01197           -0.0013       1.0185
       Factor76  |     -0.10009      0.00454           -0.0015       1.0170
       Factor77  |     -0.10462      0.00781           -0.0015       1.0155
       Factor78  |     -0.11243      0.01019           -0.0016       1.0138
       Factor79  |     -0.12262      0.02068           -0.0018       1.0120
       Factor80  |     -0.14330      0.00528           -0.0021       1.0099
       Factor81  |     -0.14858      0.00753           -0.0022       1.0078
       Factor82  |     -0.15610      0.01442           -0.0023       1.0055
       Factor83  |     -0.17053      0.03276           -0.0025       1.0030
       Factor84  |     -0.20329            .           -0.0030       1.0000
    --------------------------------------------------------------------------
    LR test: independent vs. saturated: chi2(3486)= 9.6e+05 Prob>chi2 = 0.0000

Factor loadings (pattern matrix) and unique variances

    ---------------------------------------------------------------------
        Variable |  Factor1   Factor2   Factor3   Factor4 |   Uniqueness 
    -------------+----------------------------------------+--------------
    partyrbrstmp |   0.1524    0.3401    0.5145   -0.0236 |      0.5958  
       militrank |  -0.6215    0.6216   -0.0292   -0.1567 |      0.2020  
     ldrrotation |  -0.2019    0.2056   -0.1944   -0.1666 |      0.8514  
      milconsult |  -0.4886    0.3404   -0.2951   -0.1584 |      0.5332  
    milmerit_mil |   0.1278    0.0308   -0.5460   -0.3154 |      0.5851  
    milmeritpers |  -0.1443    0.0749    0.4608    0.3295 |      0.6526  
      milnotrial |  -0.1283    0.2524    0.3871    0.3265 |      0.6634  
      plebiscite |   0.0358    0.2430    0.3136   -0.2040 |      0.7997  
        heirclan |  -0.1433   -0.3090    0.2337    0.3031 |      0.7375  
      officepers |  -0.1025   -0.0701    0.6428    0.0904 |      0.5633  
     paramilpers |  -0.2039   -0.1441    0.4767    0.1087 |      0.6986  
    ParamilParty |   0.3837    0.1255   -0.0983   -0.0752 |      0.8217  
     ParamilFReb |  -0.0790    0.1422   -0.2491    0.0881 |      0.9037  
    supportparty |   0.7573    0.5066    0.3109    0.0521 |      0.0705  
     partyleader |   0.6330    0.3090    0.2351    0.1007 |      0.4384  
     localorgzns |   0.7619    0.4631    0.2255    0.0366 |      0.1528  
       partymins |   0.7410    0.3943    0.1497    0.0628 |      0.2692  
       excomcivn |   0.7404    0.3218    0.0936    0.0267 |      0.3387  
     multiethnic |   0.5900    0.3030    0.0330   -0.0700 |      0.5541  
      monoethnic |   0.1044    0.1795    0.2998    0.1427 |      0.8466  
       heirparty |   0.7785    0.1200   -0.2060   -0.0489 |      0.3348  
      heirfamily |  -0.3792   -0.3473    0.4306    0.2340 |      0.4955  
      legcompetn |   0.3663   -0.0329    0.1778   -0.3405 |      0.7172  
    leaderrela~s |  -0.1426   -0.2884    0.3948    0.2387 |      0.6836  
       leaderciv |   0.5252   -0.6714    0.0080   -0.3247 |      0.1680  
       leadermil |  -0.6728    0.6587    0.0377   -0.1399 |      0.0924  
     leaderrebel |   0.2086    0.0605   -0.0747    0.7740 |      0.3482  
         heirciv |   0.6936   -0.2316   -0.2209   -0.1202 |      0.4019  
          cabciv |   0.4193   -0.4247    0.0792   -0.0333 |      0.6364  
          cabmil |  -0.4073    0.5057   -0.1107    0.0272 |      0.5654  
      partymilit |   0.6097    0.1412   -0.2651    0.2849 |      0.4569  
       ldrPriorD |   0.2920   -0.1581    0.2768   -0.4312 |      0.6272  
        ldrParty |   0.5190    0.0237   -0.3317   -0.1272 |      0.6039  
          ldrMil |  -0.6590    0.5639   -0.0305   -0.1680 |      0.2186  
        ldrRebel |   0.1747    0.0620   -0.0846    0.7029 |      0.4644  
          ldrCiv |   0.0694   -0.0116    0.0360   -0.0713 |      0.9887  
        ldrOther |  -0.0366    0.0064    0.1735    0.0215 |      0.9680  
        ldrForgn |   0.1496   -0.0467    0.0807    0.0593 |      0.9654  
        ldrHered |  -0.3065   -0.7332    0.0865    0.1296 |      0.3442  
        SeizCoup |  -0.5907    0.5064    0.0613   -0.1472 |      0.3692  
       SeizRebel |   0.2553   -0.0494   -0.2595    0.6176 |      0.4836  
       SeizUpris |  -0.0031    0.0219   -0.1023   -0.0674 |      0.9845  
        SeizElec |   0.3685   -0.1477    0.2212   -0.4748 |      0.5681  
        SeizSucc |  -0.0347    0.0487    0.0662   -0.0503 |      0.9895  
         SeizFam |  -0.2215   -0.5210    0.0709    0.0669 |      0.6701  
     PartyhNoWin |   0.1465    0.1491    0.0481    0.1544 |      0.9302  
       PartyhWin |   0.0487    0.0095    0.0430   -0.0593 |      0.9922  
       PartyhReb |   0.3491    0.1304   -0.3335    0.4946 |      0.5053  
    PartyhPrio~m |   0.4530   -0.0293    0.1967   -0.4223 |      0.5768  
    PartyhNopa~y |  -0.7573   -0.5066   -0.3109   -0.0521 |      0.0705  
      PartyhElec |  -0.0001   -0.0163    0.1340   -0.0234 |      0.9812  
    MilPartyAlly |  -0.0813    0.2145    0.0949   -0.0509 |      0.9358  
      MilPartyNo |  -0.6357    0.1397   -0.4156   -0.1721 |      0.3741  
    MilPartyPr~r |  -0.0690    0.3698    0.0967   -0.0143 |      0.8489  
      nomilitary |   0.0896   -0.2075    0.0966   -0.1627 |      0.9131  
    milethnic_~e |   0.1377    0.2617   -0.2516   -0.0263 |      0.8486  
    milethnic~ro |  -0.0583   -0.2327    0.0794    0.0584 |      0.9327  
    milethnic~mo |  -0.1627    0.0532    0.1916    0.0485 |      0.9316  
    sectyapp_p~y |   0.4428    0.0735   -0.4441    0.0919 |      0.5929  
    sectyapppers |  -0.1134   -0.1562    0.5979    0.1684 |      0.5770  
    ElecldrPrD~t |   0.0377   -0.0396    0.0533   -0.0356 |      0.9929  
    ElecldrPrDem |   0.0890   -0.1230    0.0906   -0.1958 |      0.9304  
      ElecldrNot |  -0.1612    0.2997   -0.4433    0.2383 |      0.6309  
       Elecldr1C |   0.1403    0.2305    0.3374   -0.0941 |      0.8045  
       Elecldr1F |   0.1215    0.0244    0.0388    0.0968 |      0.9738  
     ElecldrMLeg |   0.1840   -0.0539   -0.0133   -0.1743 |      0.9327  
    ElecldrMExec |   0.1167    0.0979    0.0627   -0.1932 |      0.9356  
    legnoms_in~t |  -0.0005    0.0107   -0.1495    0.3112 |      0.8807  
    legnoms_veto |   0.1775    0.1343    0.2352   -0.1984 |      0.8557  
    legnoms_no~o |   0.0786   -0.2559   -0.0894   -0.1951 |      0.8823  
    legnoms_pr~m |   0.0779   -0.0930    0.0718   -0.1826 |      0.9468  
      LdrexHighR |  -0.5527    0.5460   -0.0716   -0.1625 |      0.3649  
       LdrexLowR |  -0.2722    0.2450    0.1455   -0.0083 |      0.8446  
      LdrexRebel |   0.1910    0.0348   -0.0675    0.7774 |      0.3534  
      LdrexDemEl |   0.3710   -0.1756    0.2646   -0.4552 |      0.5543  
      LdrexParty |   0.5554    0.0022   -0.3519   -0.0846 |      0.5605  
      LdrexLoyal |   0.0287    0.0055    0.0560   -0.0186 |      0.9957  
      LdrexReltv |  -0.0049    0.0212    0.1398    0.0384 |      0.9785  
     LdrexRulFam |  -0.3145   -0.7639    0.0980    0.1287 |      0.2914  
      LdrexOther |  -0.0151   -0.0825    0.0258   -0.0524 |      0.9895  
    partye~mpers |   0.1682    0.3500    0.5464    0.1088 |      0.5388  
    partyexcom~n |   0.3926    0.0904   -0.2125    0.0056 |      0.7925  
    partyexcom~e |   0.3055    0.0228   -0.2149   -0.0542 |      0.8570  
     createparty |  -0.1643    0.3376    0.4166    0.0735 |      0.6801  
    ---------------------------------------------------------------------

.                 rotate, promax(3) 

Factor analysis/correlation                      Number of obs    =      3,896
    Method: principal factors                    Retained factors =          4
    Rotation: oblique promax (Kaiser off)        Number of params =        330

    --------------------------------------------------------------------------
         Factor  |     Variance   Proportion    Rotated factors are correlated
    -------------+------------------------------------------------------------
        Factor1  |      9.76146       0.1430
        Factor2  |      9.24187       0.1354
        Factor3  |      6.19690       0.0908
        Factor4  |      4.97923       0.0729
    --------------------------------------------------------------------------
    LR test: independent vs. saturated: chi2(3486)= 9.6e+05 Prob>chi2 = 0.0000

Rotated factor loadings (pattern matrix) and unique variances

    ---------------------------------------------------------------------
        Variable |  Factor1   Factor2   Factor3   Factor4 |   Uniqueness 
    -------------+----------------------------------------+--------------
    partyrbrstmp |   0.5137   -0.1757    0.4004   -0.1053 |      0.5958  
       militrank |  -0.0020   -0.8939   -0.0208   -0.1260 |      0.2020  
     ldrrotation |  -0.0658   -0.3170   -0.2175   -0.1108 |      0.8514  
      milconsult |  -0.1990   -0.5966   -0.2641   -0.0832 |      0.5332  
    milmerit_mil |  -0.0916   -0.0199   -0.6420   -0.1723 |      0.5851  
    milmeritpers |   0.1251   -0.0810    0.5595    0.2138 |      0.6526  
      milnotrial |   0.2333   -0.2252    0.4673    0.2436 |      0.6634  
      plebiscite |   0.2928   -0.1922    0.1880   -0.2491 |      0.7997  
        heirclan |  -0.2192    0.2272    0.3821    0.2081 |      0.7375  
      officepers |   0.1066    0.0452    0.6551   -0.0714 |      0.5633  
     paramilpers |  -0.0691    0.0436    0.5362   -0.0268 |      0.6986  
    ParamilParty |   0.3080    0.0974   -0.2089   -0.0201 |      0.8217  
     ParamilFReb |  -0.0369   -0.1664   -0.2007    0.1513 |      0.9037  
    supportparty |   0.9678    0.0250    0.0947    0.0611 |      0.0705  
     partyleader |   0.7232    0.1226    0.0880    0.1029 |      0.4384  
     localorgzns |   0.9109    0.0578    0.0142    0.0623 |      0.1528  
       partymins |   0.8240    0.1030   -0.0354    0.0985 |      0.2692  
       excomcivn |   0.7530    0.1563   -0.0915    0.0701 |      0.3387  
     multiethnic |   0.6147    0.0724   -0.1472   -0.0197 |      0.5541  
      monoethnic |   0.3025   -0.0590    0.2848    0.0905 |      0.8466  
       heirparty |   0.5331    0.3227   -0.3802    0.0505 |      0.3348  
      heirfamily |  -0.3389    0.1278    0.5939    0.0798 |      0.4955  
      legcompetn |   0.2719    0.2030   -0.0212   -0.3568 |      0.7172  
    leaderrela~s |  -0.1515    0.2109    0.5073    0.1099 |      0.6836  
       leaderciv |  -0.1216    0.8222   -0.1357   -0.3477 |      0.1680  
       leadermil |   0.0129   -0.9486    0.0534   -0.1247 |      0.0924  
     leaderrebel |   0.1839    0.1564    0.1418    0.7881 |      0.3482  
         heirciv |   0.2248    0.5608   -0.3620   -0.0497 |      0.4019  
          cabciv |   0.0131    0.5939    0.0230   -0.0647 |      0.6364  
          cabmil |   0.0401   -0.6576   -0.0656    0.0734 |      0.5654  
      partymilit |   0.4261    0.2466   -0.2904    0.3827 |      0.4569  
       ldrPriorD |   0.1660    0.2604    0.0690   -0.4826 |      0.6272  
        ldrParty |   0.2463    0.2409   -0.4593   -0.0185 |      0.6039  
          ldrMil |  -0.0680   -0.8680   -0.0117   -0.1436 |      0.2186  
        ldrRebel |   0.1562    0.1268    0.1155    0.7194 |      0.4644  
          ldrCiv |   0.0483    0.0423   -0.0036   -0.0753 |      0.9887  
        ldrOther |   0.0402   -0.0145    0.1749   -0.0205 |      0.9680  
        ldrForgn |   0.0979    0.1352    0.0693    0.0430 |      0.9654  
        ldrHered |  -0.6794    0.4627    0.2673    0.0287 |      0.3442  
        SeizCoup |  -0.0298   -0.7738    0.0727   -0.1458 |      0.3692  
       SeizRebel |   0.0700    0.2466   -0.0796    0.6714 |      0.4836  
       SeizUpris |  -0.0244   -0.0336   -0.1193   -0.0405 |      0.9845  
        SeizElec |   0.2039    0.2868   -0.0140   -0.5074 |      0.5681  
        SeizSucc |   0.0314   -0.0631    0.0462   -0.0621 |      0.9895  
         SeizFam |  -0.4828    0.3241    0.1909   -0.0065 |      0.6701  
     PartyhNoWin |   0.2236   -0.0213    0.0501    0.1597 |      0.9302  
       PartyhWin |   0.0519    0.0146    0.0089   -0.0644 |      0.9922  
       PartyhReb |   0.2278    0.1300   -0.2286    0.5885 |      0.5053  
    PartyhPrio~m |   0.3362    0.2401   -0.0494   -0.4362 |      0.5768  
    PartyhNopa~y |  -0.9678   -0.0250   -0.0947   -0.0611 |      0.0705  
      PartyhElec |   0.0338    0.0178    0.1182   -0.0552 |      0.9812  
    MilPartyAlly |   0.1248   -0.2273    0.0640   -0.0577 |      0.9358  
      MilPartyNo |  -0.4786   -0.5190   -0.3282   -0.0933 |      0.3741  
    MilPartyPr~r |   0.2426   -0.3463    0.0584   -0.0087 |      0.8489  
      nomilitary |  -0.0563    0.2104    0.0392   -0.1938 |      0.9131  
    milethnic_~e |   0.1864   -0.1582   -0.2991    0.0617 |      0.8486  
    milethnic~ro |  -0.1710    0.1737    0.1309    0.0160 |      0.9327  
    milethnic~mo |  -0.0048   -0.1209    0.2215   -0.0009 |      0.9316  
    sectyapp_p~y |   0.1989    0.1767   -0.4796    0.2214 |      0.5929  
    sectyapppers |   0.0270    0.1183    0.6514    0.0072 |      0.5770  
    ElecldrPrD~t |   0.0149    0.0530    0.0341   -0.0484 |      0.9929  
    ElecldrPrDem |  -0.0014    0.1348    0.0134   -0.2177 |      0.9304  
      ElecldrNot |  -0.0442   -0.3372   -0.3307    0.3515 |      0.6309  
       Elecldr1C |   0.3664   -0.1081    0.2270   -0.1429 |      0.8045  
       Elecldr1F |   0.1152    0.0618    0.0410    0.0939 |      0.9738  
     ElecldrMLeg |   0.0753    0.1276   -0.1028   -0.1619 |      0.9327  
    ElecldrMExec |   0.1607   -0.0362   -0.0416   -0.1885 |      0.9356  
    legnoms_in~t |  -0.0332    0.0204   -0.0350    0.3389 |      0.8807  
    legnoms_veto |   0.2859   -0.0242    0.1001   -0.2272 |      0.8557  
    legnoms_no~o |  -0.1622    0.2313   -0.1365   -0.1871 |      0.8823  
    legnoms_pr~m |   0.0059    0.1040   -0.0007   -0.1984 |      0.9468  
      LdrexHighR |  -0.0230   -0.7944   -0.0678   -0.1248 |      0.3649  
       LdrexLowR |   0.0367   -0.3532    0.1610   -0.0352 |      0.8446  
      LdrexRebel |   0.1569    0.1688    0.1561    0.7867 |      0.3534  
      LdrexDemEl |   0.2019    0.3161    0.0354   -0.5005 |      0.5543  
      LdrexParty |   0.2504    0.2838   -0.4688    0.0279 |      0.5605  
      LdrexLoyal |   0.0416    0.0123    0.0392   -0.0291 |      0.9957  
      LdrexReltv |   0.0608   -0.0086    0.1412    0.0067 |      0.9785  
     LdrexRulFam |  -0.7021    0.4844    0.2827    0.0222 |      0.2914  
      LdrexOther |  -0.0604    0.0558    0.0185   -0.0648 |      0.9895  
    partye~mpers |   0.5469   -0.1574    0.4702    0.0182 |      0.5388  
    partyexcom~n |   0.2533    0.1357   -0.2856    0.0826 |      0.7925  
    partyexcom~e |   0.1451    0.1359   -0.2826    0.0146 |      0.8570  
     createparty |   0.2693   -0.3460    0.4075   -0.0046 |      0.6801  
    ---------------------------------------------------------------------

Factor rotation matrix

    --------------------------------------------------
                 | Factor1  Factor2  Factor3  Factor4 
    -------------+------------------------------------
         Factor1 |  0.7971   0.7013  -0.3346  -0.0002 
         Factor2 |  0.5478  -0.7096  -0.1348   0.1140 
         Factor3 |  0.2523   0.0573   0.9006  -0.3271 
         Factor4 | -0.0301   0.0374   0.2427   0.9381 
    --------------------------------------------------

.                 predict pweeks1 pweeks2 pweeks3 pweeks4 
(regression scoring assumed)

Scoring coefficients (method = regression; based on promax(3) rotated factors)

    ------------------------------------------------------
        Variable |  Factor1   Factor2   Factor3   Factor4 
    -------------+----------------------------------------
    partyrbrstmp |  0.02677  -0.01736   0.04110  -0.01644 
       militrank |  0.03210  -0.01497  -0.03939   0.00190 
     ldrrotation |  0.00180  -0.02080  -0.03530  -0.00235 
      milconsult | -0.01131  -0.02480  -0.04609  -0.00126 
    milmerit_mil | -0.03096  -0.00768  -0.13202  -0.07147 
    milmeritpers | -0.01143  -0.01008   0.11390   0.00739 
      milnotrial |  0.01021  -0.00580   0.02964   0.01094 
      plebiscite |  0.00932   0.01039   0.02268  -0.00912 
        heirclan |  0.00098   0.00905   0.02543   0.00400 
      officepers |  0.01976   0.01140   0.06774  -0.01459 
     paramilpers | -0.00657   0.00341   0.05598  -0.00679 
    ParamilParty |  0.02179   0.00480  -0.02928  -0.01167 
     ParamilFReb | -0.00546  -0.00979  -0.02395   0.01125 
    supportparty |  0.47061  -0.03425   0.07788   0.02371 
     partyleader |  0.00528   0.01039   0.01815  -0.00696 
     localorgzns |  0.06064   0.00801  -0.02184   0.01200 
       partymins |  0.01953   0.02452  -0.00844   0.02286 
       excomcivn |  0.02822  -0.00253  -0.01261  -0.00736 
     multiethnic |  0.19238   0.05041   0.02605  -0.02449 
      monoethnic |  0.13082   0.03353   0.09100   0.02491 
       heirparty |  0.05428   0.05400  -0.09490   0.00840 
      heirfamily | -0.01080  -0.00305   0.08334   0.00360 
      legcompetn |  0.02834   0.01521  -0.00599  -0.10085 
    leaderrela~s |  0.00558   0.01457   0.05368   0.00027 
       leaderciv |  0.00000   0.00000   0.00000   0.00000 
       leadermil |  0.03465  -0.39795   0.07320   0.07028 
     leaderrebel | -0.00051  -0.05198   0.02372   0.23323 
         heirciv |  0.03486   0.01429  -0.06357  -0.00324 
          cabciv |  0.01207   0.05304  -0.00297  -0.02882 
          cabmil |  0.00927  -0.08270  -0.02061   0.00831 
      partymilit |  0.01672   0.00784  -0.03322   0.04713 
       ldrPriorD |  0.06128   0.14136   0.02617  -0.11335 
        ldrParty |  0.05974   0.15653  -0.06603   0.00834 
          ldrMil |  0.00000   0.00000   0.00000   0.00000 
        ldrRebel |  0.05039   0.10377   0.04250   0.19403 
          ldrCiv | -0.00216   0.03488   0.00258  -0.02105 
        ldrOther |  0.00962   0.05695   0.04158   0.00126 
        ldrForgn |  0.04657   0.11904   0.00934   0.02102 
        ldrHered | -0.07136   0.15078   0.04904   0.07182 
        SeizCoup | -0.00038  -0.03541  -0.00465  -0.03375 
       SeizRebel |  0.01165   0.00943  -0.03813   0.07831 
       SeizUpris |  0.01085   0.00828  -0.01418  -0.00623 
        SeizElec |  0.00610   0.02407   0.01830  -0.06745 
        SeizSucc |  0.00016   0.01000   0.00695  -0.01019 
         SeizFam | -0.00756   0.01338  -0.00094  -0.01218 
     PartyhNoWin | -0.01876   0.00688   0.00480   0.00605 
       PartyhWin | -0.03711  -0.00416   0.00058  -0.02740 
       PartyhReb | -0.03952   0.03010  -0.07323   0.09319 
    PartyhPrio~m | -0.02279   0.03961  -0.03099  -0.12986 
    PartyhNopa~y |  0.00000   0.00000   0.00000   0.00000 
      PartyhElec | -0.03465  -0.00027   0.01416  -0.00825 
    MilPartyAlly | -0.00558   0.03296   0.00673   0.00429 
      MilPartyNo | -0.02394   0.02293  -0.04914  -0.02340 
    MilPartyPr~r | -0.01464   0.02635  -0.00798   0.00864 
      nomilitary | -0.03021   0.04138   0.02670  -0.06559 
    milethnic_~e |  0.00000   0.00000   0.00000   0.00000 
    milethnic~ro | -0.04818   0.05439   0.09191   0.01155 
    milethnic~mo | -0.02008   0.00973   0.08847  -0.00271 
    sectyapp_p~y | -0.00433   0.01512  -0.04911   0.03768 
    sectyapppers | -0.00696   0.01584   0.12654  -0.00620 
    ElecldrPrD~t | -0.01281  -0.02428   0.00756   0.01543 
    ElecldrPrDem | -0.00830  -0.03203   0.01679  -0.02647 
      ElecldrNot | -0.05052  -0.20915  -0.05970   0.20526 
       Elecldr1C |  0.02022  -0.17018   0.07254   0.02914 
       Elecldr1F | -0.00031  -0.06045   0.01910   0.05886 
     ElecldrMLeg | -0.02244  -0.05442  -0.01046   0.00597 
    ElecldrMExec | -0.01073  -0.11052   0.01249   0.01304 
    legnoms_in~t | -0.01425  -0.00091  -0.00965   0.01863 
    legnoms_veto |  0.02624  -0.00784   0.02436  -0.02764 
    legnoms_no~o | -0.02703   0.02974  -0.01843   0.00030 
    legnoms_pr~m | -0.01209   0.01133   0.00220  -0.00353 
      LdrexHighR |  0.00000   0.00000   0.00000   0.00000 
       LdrexLowR |  0.02508   0.06329   0.02551   0.00820 
      LdrexRebel |  0.00565   0.12479   0.03111   0.21124 
      LdrexDemEl |  0.04396   0.16523   0.00565  -0.06976 
      LdrexParty |  0.05007   0.19612  -0.10018   0.09134 
      LdrexLoyal | -0.01029   0.03980   0.01720  -0.00169 
      LdrexReltv |  0.00093   0.04749   0.04065   0.02903 
     LdrexRulFam | -0.11379   0.10985   0.13573   0.09618 
      LdrexOther | -0.00672   0.05329  -0.00332   0.00558 
    partye~mpers |  0.03982  -0.02874   0.14015  -0.01242 
    partyexcom~n | -0.02084  -0.00543  -0.02597   0.01326 
    partyexcom~e | -0.03055   0.00502  -0.02828  -0.00043 
     createparty | -0.06709   0.03928   0.04299  -0.02109 
    ------------------------------------------------------


.                 factor $allvar1 $allvar2 $allvar3 $allvar4 $allvar5 $allvar6 $allvar
> 7 $allvar8 $allvar9 $allvar10 Junta Strongman Machine Boss Other, factors(4)        
>         
(obs=3,738)
(collinear variables specified)

Factor analysis/correlation                      Number of obs    =      3,738
    Method: principal factors                    Retained factors =          4
    Rotation: (unrotated)                        Number of params =        350

    --------------------------------------------------------------------------
         Factor  |   Eigenvalue   Difference        Proportion   Cumulative
    -------------+------------------------------------------------------------
        Factor1  |     11.93719      4.42461            0.1642       0.1642
        Factor2  |      7.51258      1.70045            0.1034       0.2676
        Factor3  |      5.81213      0.83769            0.0800       0.3475
        Factor4  |      4.97444      1.78203            0.0684       0.4160
        Factor5  |      3.19240      0.50860            0.0439       0.4599
        Factor6  |      2.68381      0.14375            0.0369       0.4968
        Factor7  |      2.54006      0.38931            0.0349       0.5318
        Factor8  |      2.15075      0.08333            0.0296       0.5613
        Factor9  |      2.06742      0.19265            0.0284       0.5898
       Factor10  |      1.87478      0.11201            0.0258       0.6156
       Factor11  |      1.76276      0.20060            0.0243       0.6398
       Factor12  |      1.56216      0.02317            0.0215       0.6613
       Factor13  |      1.53898      0.03765            0.0212       0.6825
       Factor14  |      1.50133      0.07144            0.0207       0.7032
       Factor15  |      1.42989      0.10025            0.0197       0.7228
       Factor16  |      1.32964      0.09415            0.0183       0.7411
       Factor17  |      1.23549      0.05379            0.0170       0.7581
       Factor18  |      1.18170      0.02106            0.0163       0.7744
       Factor19  |      1.16064      0.07125            0.0160       0.7903
       Factor20  |      1.08939      0.06493            0.0150       0.8053
       Factor21  |      1.02446      0.05384            0.0141       0.8194
       Factor22  |      0.97062      0.03388            0.0134       0.8328
       Factor23  |      0.93674      0.05859            0.0129       0.8457
       Factor24  |      0.87816      0.01418            0.0121       0.8577
       Factor25  |      0.86398      0.05429            0.0119       0.8696
       Factor26  |      0.80969      0.03595            0.0111       0.8808
       Factor27  |      0.77374      0.02924            0.0106       0.8914
       Factor28  |      0.74449      0.02837            0.0102       0.9017
       Factor29  |      0.71612      0.06165            0.0099       0.9115
       Factor30  |      0.65447      0.03585            0.0090       0.9205
       Factor31  |      0.61861      0.02317            0.0085       0.9290
       Factor32  |      0.59544      0.01444            0.0082       0.9372
       Factor33  |      0.58100      0.03133            0.0080       0.9452
       Factor34  |      0.54967      0.02602            0.0076       0.9528
       Factor35  |      0.52364      0.07926            0.0072       0.9600
       Factor36  |      0.44439      0.00875            0.0061       0.9661
       Factor37  |      0.43564      0.05165            0.0060       0.9721
       Factor38  |      0.38398      0.02998            0.0053       0.9774
       Factor39  |      0.35401      0.01962            0.0049       0.9822
       Factor40  |      0.33439      0.01345            0.0046       0.9868
       Factor41  |      0.32094      0.01184            0.0044       0.9912
       Factor42  |      0.30910      0.01053            0.0043       0.9955
       Factor43  |      0.29858      0.04144            0.0041       0.9996
       Factor44  |      0.25713      0.01264            0.0035       1.0031
       Factor45  |      0.24449      0.04347            0.0034       1.0065
       Factor46  |      0.20103      0.02013            0.0028       1.0093
       Factor47  |      0.18089      0.01698            0.0025       1.0118
       Factor48  |      0.16392      0.02440            0.0023       1.0140
       Factor49  |      0.13951      0.02185            0.0019       1.0159
       Factor50  |      0.11767      0.01459            0.0016       1.0176
       Factor51  |      0.10307      0.00818            0.0014       1.0190
       Factor52  |      0.09489      0.00763            0.0013       1.0203
       Factor53  |      0.08726      0.02324            0.0012       1.0215
       Factor54  |      0.06403      0.00086            0.0009       1.0224
       Factor55  |      0.06317      0.01324            0.0009       1.0232
       Factor56  |      0.04993      0.00610            0.0007       1.0239
       Factor57  |      0.04383      0.01171            0.0006       1.0245
       Factor58  |      0.03212      0.01202            0.0004       1.0250
       Factor59  |      0.02010      0.00289            0.0003       1.0252
       Factor60  |      0.01720      0.00568            0.0002       1.0255
       Factor61  |      0.01152      0.00968            0.0002       1.0256
       Factor62  |      0.00184      0.00098            0.0000       1.0257
       Factor63  |      0.00086      0.00086            0.0000       1.0257
       Factor64  |      0.00000      0.00000            0.0000       1.0257
       Factor65  |      0.00000      0.00000            0.0000       1.0257
       Factor66  |     -0.00000      0.00000           -0.0000       1.0257
       Factor67  |     -0.00000      0.00000           -0.0000       1.0257
       Factor68  |     -0.00000      0.00637           -0.0000       1.0257
       Factor69  |     -0.00637      0.00290           -0.0001       1.0256
       Factor70  |     -0.00928      0.00876           -0.0001       1.0255
       Factor71  |     -0.01804      0.00862           -0.0002       1.0252
       Factor72  |     -0.02665      0.00186           -0.0004       1.0248
       Factor73  |     -0.02851      0.01339           -0.0004       1.0244
       Factor74  |     -0.04190      0.01775           -0.0006       1.0239
       Factor75  |     -0.05965      0.00706           -0.0008       1.0230
       Factor76  |     -0.06671      0.00604           -0.0009       1.0221
       Factor77  |     -0.07275      0.00950           -0.0010       1.0211
       Factor78  |     -0.08224      0.00271           -0.0011       1.0200
       Factor79  |     -0.08495      0.01632           -0.0012       1.0188
       Factor80  |     -0.10128      0.00130           -0.0014       1.0174
       Factor81  |     -0.10257      0.00638           -0.0014       1.0160
       Factor82  |     -0.10895      0.00840           -0.0015       1.0145
       Factor83  |     -0.11735      0.01710           -0.0016       1.0129
       Factor84  |     -0.13445      0.00839           -0.0018       1.0111
       Factor85  |     -0.14284      0.00524           -0.0020       1.0091
       Factor86  |     -0.14808      0.00517           -0.0020       1.0071
       Factor87  |     -0.15324      0.01305           -0.0021       1.0050
       Factor88  |     -0.16630      0.02740           -0.0023       1.0027
       Factor89  |     -0.19370            .           -0.0027       1.0000
    --------------------------------------------------------------------------
    LR test: independent vs. saturated: chi2(3916)=       . Prob>chi2 =      .

Factor loadings (pattern matrix) and unique variances

    ---------------------------------------------------------------------
        Variable |  Factor1   Factor2   Factor3   Factor4 |   Uniqueness 
    -------------+----------------------------------------+--------------
    partyrbrstmp |   0.1229    0.3563    0.4976   -0.0486 |      0.6080  
       militrank |  -0.6461    0.5955   -0.0751   -0.1647 |      0.1952  
     ldrrotation |  -0.2081    0.1875   -0.2351   -0.1735 |      0.8362  
      milconsult |  -0.4921    0.3128   -0.3318   -0.1535 |      0.5264  
    milmerit_mil |   0.1450    0.0163   -0.5619   -0.2859 |      0.5813  
    milmeritpers |  -0.1624    0.0904    0.4736    0.3093 |      0.6455  
      milnotrial |  -0.1513    0.2679    0.3978    0.3123 |      0.6495  
      plebiscite |   0.0194    0.2438    0.3054   -0.2205 |      0.7983  
        heirclan |  -0.1464   -0.3012    0.2256    0.2820 |      0.7574  
      officepers |  -0.1228   -0.0527    0.6413    0.0620 |      0.5671  
     paramilpers |  -0.2176   -0.1397    0.4841    0.0931 |      0.6901  
    ParamilParty |   0.3791    0.1416   -0.0872   -0.0655 |      0.8243  
     ParamilFReb |  -0.0723    0.1292   -0.2573    0.0882 |      0.9041  
    supportparty |   0.7148    0.5587    0.3150    0.0174 |      0.0774  
     partyleader |   0.6165    0.3474    0.2405    0.0801 |      0.4350  
     localorgzns |   0.7285    0.5124    0.2237    0.0126 |      0.1565  
       partymins |   0.7113    0.4421    0.1564    0.0454 |      0.2721  
       excomcivn |   0.7123    0.3629    0.1090    0.0053 |      0.3490  
     multiethnic |   0.5761    0.3349    0.0339   -0.0536 |      0.5519  
      monoethnic |   0.0703    0.1934    0.3007    0.0847 |      0.8601  
       heirparty |   0.7749    0.1538   -0.1834   -0.0498 |      0.3397  
      heirfamily |  -0.3814   -0.3456    0.4373    0.2315 |      0.4903  
      legcompetn |   0.3529   -0.0220    0.1661   -0.3556 |      0.7210  
    leaderrela~s |  -0.1453   -0.2797    0.3959    0.2268 |      0.6925  
       leaderciv |   0.5545   -0.6556    0.0297   -0.3023 |      0.1705  
       leadermil |  -0.7003    0.6300   -0.0133   -0.1506 |      0.0897  
     leaderrebel |   0.2033    0.0838   -0.0288    0.7609 |      0.3718  
         heirciv |   0.7058   -0.2096   -0.2005   -0.1095 |      0.4057  
          cabciv |   0.4354   -0.4095    0.1082   -0.0452 |      0.6290  
          cabmil |  -0.4280    0.4891   -0.1411    0.0379 |      0.5563  
      partymilit |   0.6143    0.1696   -0.2387    0.3005 |      0.4466  
       ldrPriorD |   0.2879   -0.1494    0.2678   -0.4559 |      0.6152  
        ldrParty |   0.5252    0.0390   -0.3215   -0.1113 |      0.6069  
          ldrMil |  -0.6841    0.5347   -0.0717   -0.1715 |      0.2116  
        ldrRebel |   0.1718    0.0837   -0.0494    0.6980 |      0.4739  
          ldrCiv |   0.0664   -0.0115    0.0475   -0.0634 |      0.9892  
        ldrOther |  -0.0399    0.0030    0.1689    0.0174 |      0.9696  
        ldrForgn |   0.1592   -0.0419    0.1041    0.0567 |      0.9588  
        ldrHered |  -0.2835   -0.7345    0.0846    0.1510 |      0.3502  
        SeizCoup |  -0.6145    0.4869    0.0275   -0.1469 |      0.3631  
       SeizRebel |   0.2610   -0.0284   -0.2374    0.6216 |      0.4883  
       SeizUpris |  -0.0057    0.0153   -0.0865   -0.0563 |      0.9891  
        SeizElec |   0.3693   -0.1355    0.2083   -0.4947 |      0.5571  
        SeizSucc |  -0.0351    0.0398    0.0642   -0.0602 |      0.9894  
         SeizFam |  -0.2057   -0.5211    0.0684    0.0830 |      0.6745  
     PartyhNoWin |   0.1264    0.1660    0.0870    0.1701 |      0.9199  
       PartyhWin |   0.0421    0.0120    0.0466   -0.0654 |      0.9916  
       PartyhReb |   0.3482    0.1511   -0.3093    0.4932 |      0.5170  
    PartyhPrio~m |   0.4476   -0.0057    0.1836   -0.4384 |      0.5737  
    PartyhNopa~y |  -0.7148   -0.5587   -0.3150   -0.0174 |      0.0774  
      PartyhElec |  -0.0003   -0.0155    0.1410   -0.0185 |      0.9795  
    MilPartyAlly |  -0.1064    0.2226    0.0850   -0.0575 |      0.9286  
      MilPartyNo |  -0.6150    0.0925   -0.4297   -0.1483 |      0.4066  
    MilPartyPr~r |  -0.0992    0.3738    0.0693   -0.0282 |      0.8448  
      nomilitary |   0.0895   -0.2052    0.0999   -0.1690 |      0.9113  
    milethnic_~e |   0.1427    0.2623   -0.2391    0.0130 |      0.8535  
    milethnic~ro |  -0.0585   -0.2347    0.0713    0.0546 |      0.9334  
    milethnic~mo |  -0.1714    0.0568    0.1832    0.0045 |      0.9338  
    sectyapp_p~y |   0.4586    0.0887   -0.4300    0.1178 |      0.5830  
    sectyapppers |  -0.1325   -0.1432    0.6019    0.1402 |      0.5800  
    ElecldrPrD~t |   0.0280   -0.0378    0.0609   -0.0481 |      0.9918  
    ElecldrPrDem |   0.0868   -0.1210    0.0916   -0.1997 |      0.9295  
      ElecldrNot |  -0.1387    0.2878   -0.4406    0.2610 |      0.6357  
       Elecldr1C |   0.1153    0.2433    0.3359   -0.1160 |      0.8012  
       Elecldr1F |   0.1056    0.0281    0.0667    0.0949 |      0.9746  
     ElecldrMLeg |   0.1873   -0.0497   -0.0253   -0.1859 |      0.9273  
    ElecldrMExec |   0.1046    0.0982    0.0464   -0.2018 |      0.9365  
    legnoms_in~t |   0.0024    0.0079   -0.1417    0.3162 |      0.8799  
    legnoms_veto |   0.1605    0.1396    0.2161   -0.2096 |      0.8641  
    legnoms_no~o |   0.0850   -0.2591   -0.0895   -0.1891 |      0.8819  
    legnoms_pr~m |   0.0754   -0.0871    0.0660   -0.1905 |      0.9461  
      LdrexHighR |  -0.5766    0.5256   -0.1085   -0.1700 |      0.3507  
       LdrexLowR |  -0.2849    0.2314    0.1224   -0.0146 |      0.8501  
      LdrexRebel |   0.1909    0.0586   -0.0229    0.7674 |      0.3707  
      LdrexDemEl |   0.3633   -0.1641    0.2621   -0.4877 |      0.5345  
      LdrexParty |   0.5680    0.0186   -0.3294   -0.0510 |      0.5659  
      LdrexLoyal |   0.0288    0.0063    0.0660   -0.0139 |      0.9946  
      LdrexReltv |  -0.0045    0.0249    0.1332    0.0424 |      0.9798  
     LdrexRulFam |  -0.2911   -0.7668    0.0965    0.1498 |      0.2956  
      LdrexOther |  -0.0146   -0.0916    0.0299   -0.0539 |      0.9876  
    partye~mpers |   0.1319    0.3731    0.5391    0.0704 |      0.5478  
    partyexcom~n |   0.3899    0.1089   -0.2046    0.0193 |      0.7939  
    partyexcom~e |   0.3022    0.0336   -0.2049   -0.0521 |      0.8628  
     createparty |  -0.1962    0.3364    0.3853    0.0462 |      0.6977  
           Junta |  -0.3602    0.2816   -0.2754   -0.1299 |      0.6983  
       Strongman |  -0.3727    0.3979    0.2562    0.0553 |      0.6340  
         Machine |   0.5446    0.0581   -0.3352    0.0423 |      0.5858  
            Boss |   0.1847    0.0823    0.2849    0.1888 |      0.8423  
           Other |   0.0799   -0.1404    0.0195   -0.2007 |      0.9333  
    ---------------------------------------------------------------------

.                 rotate, promax(3)  

Factor analysis/correlation                      Number of obs    =      3,738
    Method: principal factors                    Retained factors =          4
    Rotation: oblique promax (Kaiser off)        Number of params =        350

    --------------------------------------------------------------------------
         Factor  |     Variance   Proportion    Rotated factors are correlated
    -------------+------------------------------------------------------------
        Factor1  |      9.97712       0.1373
        Factor2  |      9.87048       0.1358
        Factor3  |      6.57303       0.0904
        Factor4  |      5.08736       0.0700
    --------------------------------------------------------------------------
    LR test: independent vs. saturated: chi2(3916)=       . Prob>chi2 =      .

Rotated factor loadings (pattern matrix) and unique variances

    ---------------------------------------------------------------------
        Variable |  Factor1   Factor2   Factor3   Factor4 |   Uniqueness 
    -------------+----------------------------------------+--------------
    partyrbrstmp |   0.5049    0.1645    0.3940   -0.1034 |      0.6080  
       militrank |   0.0116    0.9002   -0.0200   -0.1152 |      0.1952  
     ldrrotation |  -0.0670    0.3281   -0.2388   -0.1146 |      0.8362  
      milconsult |  -0.1851    0.6074   -0.2689   -0.0759 |      0.5264  
    milmerit_mil |  -0.0777    0.0323   -0.6447   -0.1631 |      0.5813  
    milmeritpers |   0.1158    0.0714    0.5636    0.2132 |      0.6455  
      milnotrial |   0.2286    0.2168    0.4757    0.2491 |      0.6495  
      plebiscite |   0.2964    0.1819    0.1974   -0.2503 |      0.7983  
        heirclan |  -0.2442   -0.2228    0.3528    0.1961 |      0.7574  
      officepers |   0.0949   -0.0524    0.6523   -0.0744 |      0.5671  
     paramilpers |  -0.0812   -0.0525    0.5431   -0.0262 |      0.6901  
    ParamilParty |   0.3145   -0.0923   -0.2003   -0.0167 |      0.8243  
     ParamilFReb |  -0.0367    0.1651   -0.2102    0.1463 |      0.9041  
    supportparty |   0.9639   -0.0133    0.0882    0.0426 |      0.0774  
     partyleader |   0.7208   -0.1260    0.0771    0.0931 |      0.4350  
     localorgzns |   0.9083   -0.0477    0.0019    0.0515 |      0.1565  
       partymins |   0.8229   -0.0912   -0.0417    0.0890 |      0.2721  
       excomcivn |   0.7502   -0.1448   -0.0914    0.0510 |      0.3490  
     multiethnic |   0.6203   -0.0691   -0.1450   -0.0009 |      0.5519  
      monoethnic |   0.2845    0.0678    0.2739    0.0470 |      0.8601  
       heirparty |   0.5397   -0.3099   -0.3782    0.0356 |      0.3397  
      heirfamily |  -0.3535   -0.1357    0.5946    0.0910 |      0.4903  
      legcompetn |   0.2661   -0.1972   -0.0266   -0.3678 |      0.7210  
    leaderrela~s |  -0.1704   -0.2179    0.4944    0.1111 |      0.6925  
       leaderciv |  -0.1204   -0.8226   -0.1315   -0.3437 |      0.1705  
       leadermil |   0.0233    0.9514    0.0516   -0.1122 |      0.0897  
     leaderrebel |   0.1667   -0.1584    0.1394    0.7685 |      0.3718  
         heirciv |   0.2240   -0.5524   -0.3646   -0.0589 |      0.4057  
          cabciv |   0.0078   -0.5977    0.0231   -0.0875 |      0.6290  
          cabmil |   0.0448    0.6626   -0.0645    0.0950 |      0.5563  
      partymilit |   0.4263   -0.2445   -0.2929    0.3843 |      0.4466  
       ldrPriorD |   0.1667   -0.2590    0.0656   -0.5014 |      0.6152  
        ldrParty |   0.2539   -0.2290   -0.4582   -0.0200 |      0.6069  
          ldrMil |  -0.0558    0.8742   -0.0049   -0.1302 |      0.2116  
        ldrRebel |   0.1412   -0.1283    0.1091    0.7094 |      0.4739  
          ldrCiv |   0.0500   -0.0454    0.0119   -0.0697 |      0.9892  
        ldrOther |   0.0327    0.0042    0.1724   -0.0172 |      0.9696  
        ldrForgn |   0.1024   -0.1491    0.0809    0.0380 |      0.9588  
        ldrHered |  -0.6938   -0.4568    0.2511    0.0459 |      0.3502  
        SeizCoup |  -0.0150    0.7789    0.0833   -0.1271 |      0.3631  
       SeizRebel |   0.0537   -0.2397   -0.1004    0.6638 |      0.4883  
       SeizUpris |  -0.0199    0.0338   -0.0975   -0.0370 |      0.9891  
        SeizElec |   0.2090   -0.2840   -0.0214   -0.5230 |      0.5571  
        SeizSucc |   0.0296    0.0543    0.0475   -0.0690 |      0.9894  
         SeizFam |  -0.4920   -0.3190    0.1801    0.0070 |      0.6745  
     PartyhNoWin |   0.2275    0.0247    0.0876    0.1718 |      0.9199  
       PartyhWin |   0.0518   -0.0115    0.0141   -0.0702 |      0.9916  
       PartyhReb |   0.2192   -0.1199   -0.2405    0.5736 |      0.5170  
    PartyhPrio~m |   0.3447   -0.2308   -0.0574   -0.4466 |      0.5737  
    PartyhNopa~y |  -0.9639    0.0133   -0.0882   -0.0426 |      0.0774  
      PartyhElec |   0.0353   -0.0261    0.1283   -0.0471 |      0.9795  
    MilPartyAlly |   0.1264    0.2422    0.0687   -0.0552 |      0.9286  
      MilPartyNo |  -0.4570    0.5129   -0.3122   -0.0791 |      0.4066  
    MilPartyPr~r |   0.2367    0.3580    0.0478   -0.0080 |      0.8448  
      nomilitary |  -0.0592   -0.2071    0.0416   -0.2017 |      0.9113  
    milethnic_~e |   0.2037    0.1530   -0.2758    0.0916 |      0.8535  
    milethnic~ro |  -0.1871   -0.1711    0.1162    0.0137 |      0.9334  
    milethnic~mo |  -0.0052    0.1265    0.2078   -0.0329 |      0.9338  
    sectyapp_p~y |   0.2090   -0.1690   -0.4825    0.2277 |      0.5830  
    sectyapppers |   0.0078   -0.1265    0.6480    0.0005 |      0.5800  
    ElecldrPrD~t |   0.0106   -0.0476    0.0400   -0.0616 |      0.9918  
    ElecldrPrDem |  -0.0011   -0.1320    0.0184   -0.2219 |      0.9295  
      ElecldrNot |  -0.0248    0.3298   -0.3303    0.3642 |      0.6357  
       Elecldr1C |   0.3646    0.1059    0.2342   -0.1497 |      0.8012  
       Elecldr1F |   0.1074   -0.0615    0.0633    0.0874 |      0.9746  
     ElecldrMLeg |   0.0757   -0.1227   -0.1165   -0.1742 |      0.9273  
    ElecldrMExec |   0.1568    0.0404   -0.0474   -0.1924 |      0.9365  
    legnoms_in~t |  -0.0448   -0.0236   -0.0426    0.3383 |      0.8799  
    legnoms_veto |   0.2785    0.0220    0.0932   -0.2265 |      0.8641  
    legnoms_no~o |  -0.1640   -0.2234   -0.1362   -0.1902 |      0.8819  
    legnoms_pr~m |   0.0081   -0.0961   -0.0032   -0.2050 |      0.9461  
      LdrexHighR |  -0.0072    0.8067   -0.0630   -0.1179 |      0.3507  
       LdrexLowR |   0.0325    0.3455    0.1565   -0.0273 |      0.8501  
      LdrexRebel |   0.1422   -0.1730    0.1518    0.7707 |      0.3707  
      LdrexDemEl |   0.2018   -0.3109    0.0349   -0.5296 |      0.5345  
      LdrexParty |   0.2621   -0.2787   -0.4562    0.0403 |      0.5659  
      LdrexLoyal |   0.0448   -0.0178    0.0507   -0.0246 |      0.9946  
      LdrexReltv |   0.0589    0.0014    0.1362    0.0179 |      0.9798  
     LdrexRulFam |  -0.7184   -0.4797    0.2664    0.0388 |      0.2956  
      LdrexOther |  -0.0659   -0.0613    0.0236   -0.0684 |      0.9876  
    partye~mpers |   0.5346    0.1511    0.4638    0.0073 |      0.5478  
    partyexcom~n |   0.2569   -0.1236   -0.2855    0.0864 |      0.7939  
    partyexcom~e |   0.1476   -0.1221   -0.2799    0.0051 |      0.8628  
     createparty |   0.2513    0.3384    0.3914   -0.0045 |      0.6977  
           Junta |  -0.1071    0.4935   -0.2370   -0.0612 |      0.6983  
       Strongman |   0.1432    0.5073    0.3084    0.0282 |      0.6340  
         Machine |   0.2730   -0.2454   -0.4327    0.1359 |      0.5858  
            Boss |   0.2674   -0.1034    0.2724    0.1458 |      0.8423  
           Other |  -0.0436   -0.1351   -0.0463   -0.2110 |      0.9333  
    ---------------------------------------------------------------------

Factor rotation matrix

    --------------------------------------------------
                 | Factor1  Factor2  Factor3  Factor4 
    -------------+------------------------------------
         Factor1 |  0.7636  -0.7340  -0.3569   0.0123 
         Factor2 |  0.5917   0.6691  -0.0975   0.1362 
         Factor3 |  0.2548  -0.1021   0.9061  -0.2781 
         Factor4 | -0.0434  -0.0562   0.2053   0.9508 
    --------------------------------------------------

.                 predict weeks1 weeks2 weeks3 weeks4     
(regression scoring assumed)

Scoring coefficients (method = regression; based on promax(3) rotated factors)

    ------------------------------------------------------
        Variable |  Factor1   Factor2   Factor3   Factor4 
    -------------+----------------------------------------
    partyrbrstmp |  0.02457   0.01400   0.03491  -0.01665 
       militrank |  0.02420   0.01379  -0.03248   0.00018 
     ldrrotation |  0.00118   0.01875  -0.03450  -0.00087 
      milconsult | -0.01136   0.02489  -0.04343  -0.00140 
    milmerit_mil | -0.03329   0.00930  -0.12554  -0.06946 
    milmeritpers | -0.01173   0.00726   0.10920   0.00765 
      milnotrial |  0.01357   0.00692   0.02452   0.01226 
      plebiscite |  0.01172  -0.00883   0.02094  -0.01011 
        heirclan |  0.00179  -0.00732   0.02190   0.00546 
      officepers |  0.01835  -0.00966   0.06235  -0.01507 
     paramilpers | -0.00337  -0.00505   0.05336  -0.00820 
    ParamilParty |  0.02508  -0.00450  -0.02606  -0.00971 
     ParamilFReb | -0.00457   0.00681  -0.02578   0.00896 
    supportparty |  0.45653   0.02766   0.08030   0.01187 
     partyleader |  0.00419  -0.01400   0.01562  -0.00465 
     localorgzns |  0.05735  -0.00980  -0.02298   0.01281 
       partymins |  0.02220  -0.02487  -0.00825   0.01987 
       excomcivn |  0.02806   0.00121  -0.01164  -0.00874 
     multiethnic |  0.19219  -0.03158   0.02363  -0.00868 
      monoethnic |  0.13296  -0.02143   0.08777   0.02025 
       heirparty |  0.05193  -0.04599  -0.09218   0.00675 
      heirfamily | -0.01304   0.00312   0.07983   0.00450 
      legcompetn |  0.02345  -0.01267  -0.00684  -0.10307 
    leaderrela~s |  0.00373  -0.01595   0.04938   0.00168 
       leaderciv |  0.00000   0.00000   0.00000   0.00000 
       leadermil |  0.03408   0.38845   0.05252   0.06140 
     leaderrebel | -0.00196   0.04885   0.03871   0.22480 
         heirciv |  0.03016  -0.01566  -0.04965  -0.00473 
          cabciv |  0.00965  -0.04489  -0.00149  -0.02871 
          cabmil |  0.00645   0.08224  -0.01827   0.01285 
      partymilit |  0.01556  -0.00942  -0.03333   0.04998 
       ldrPriorD |  0.05831  -0.14656   0.02609  -0.11093 
        ldrParty |  0.05683  -0.15952  -0.06380   0.00827 
          ldrMil |  0.00000   0.00000   0.00000   0.00000 
        ldrRebel |  0.04428  -0.10097   0.04416   0.18988 
          ldrCiv | -0.00062  -0.03737   0.00151  -0.01938 
        ldrOther |  0.00934  -0.06025   0.03954   0.00381 
        ldrForgn |  0.04613  -0.11896   0.00936   0.02179 
        ldrHered | -0.06922  -0.15167   0.04151   0.07459 
        SeizCoup |  0.00238   0.03100  -0.01016  -0.02757 
       SeizRebel |  0.01133  -0.00166  -0.04430   0.08202 
       SeizUpris |  0.01223  -0.00734  -0.01660  -0.00652 
        SeizElec |  0.00367  -0.01890   0.01393  -0.06746 
        SeizSucc | -0.00108  -0.01489   0.00327  -0.01162 
         SeizFam | -0.00793  -0.01188  -0.00162  -0.00874 
     PartyhNoWin | -0.01562  -0.00695   0.00539   0.01010 
       PartyhWin | -0.03766   0.00488  -0.00149  -0.02664 
       PartyhReb | -0.03819  -0.03291  -0.06738   0.08934 
    PartyhPrio~m | -0.02651  -0.03481  -0.03378  -0.12418 
    PartyhNopa~y |  0.00000   0.00000   0.00000   0.00000 
      PartyhElec | -0.03472  -0.00568   0.01222  -0.00638 
    MilPartyAlly | -0.00335  -0.03017   0.01040   0.00214 
      MilPartyNo | -0.01936  -0.02120  -0.04036  -0.02418 
    MilPartyPr~r | -0.01323  -0.02633  -0.00372   0.00735 
      nomilitary | -0.03298  -0.04043   0.02563  -0.06721 
    milethnic_~e |  0.00000   0.00000   0.00000   0.00000 
    milethnic~ro | -0.04981  -0.04771   0.07813   0.00368 
    milethnic~mo | -0.01969  -0.00621   0.07489  -0.01215 
    sectyapp_p~y | -0.00562  -0.00986  -0.04996   0.03718 
    sectyapppers | -0.00440  -0.01398   0.11435  -0.00730 
    ElecldrPrD~t | -0.00925   0.02336   0.00505   0.01051 
    ElecldrPrDem |  0.00028   0.03520   0.00524  -0.02857 
      ElecldrNot | -0.01983   0.21014  -0.08319   0.19411 
       Elecldr1C |  0.04374   0.16822   0.04533   0.02376 
       Elecldr1F |  0.00973   0.05726   0.01633   0.05417 
     ElecldrMLeg | -0.01197   0.05800  -0.02458   0.00138 
    ElecldrMExec |  0.00697   0.11307  -0.00777   0.00848 
    legnoms_in~t | -0.01227   0.00286  -0.01165   0.01690 
    legnoms_veto |  0.02599   0.00542   0.02545  -0.02439 
    legnoms_no~o | -0.02809  -0.03194  -0.01337   0.00102 
    legnoms_pr~m | -0.01092  -0.01091   0.00413  -0.00306 
      LdrexHighR |  0.00000   0.00000   0.00000   0.00000 
       LdrexLowR |  0.01959  -0.06498   0.02584   0.01052 
      LdrexRebel |  0.00878  -0.13497   0.01219   0.19899 
      LdrexDemEl |  0.03854  -0.15987   0.00668  -0.07975 
      LdrexParty |  0.04585  -0.18761  -0.09065   0.08745 
      LdrexLoyal | -0.01293  -0.04428   0.01662  -0.00231 
      LdrexReltv | -0.00074  -0.04726   0.03500   0.02961 
     LdrexRulFam | -0.12105  -0.11183   0.12971   0.10370 
      LdrexOther | -0.00819  -0.05162  -0.00014   0.00316 
    partye~mpers |  0.04459   0.03055   0.13471  -0.00639 
    partyexcom~n | -0.01299   0.01097  -0.02747   0.01745 
    partyexcom~e | -0.02574   0.00211  -0.02966   0.00006 
     createparty | -0.06707  -0.04078   0.04138  -0.02030 
           Junta | -0.04319   0.02166  -0.01627   0.00381 
       Strongman | -0.01984  -0.00318   0.09323   0.03251 
         Machine |  0.00145  -0.05646  -0.02236   0.03756 
            Boss | -0.00610  -0.02678   0.09709   0.03923 
           Other | -0.03055  -0.03752   0.02624  -0.01646 
    ------------------------------------------------------


.                 pwcorr pr1 pr2 pr3 pr4 pweeks1 pweeks2 pweeks3 pweeks4

             |      pr1      pr2      pr3      pr4  pweeks1  pweeks2  pweeks3
-------------+---------------------------------------------------------------
         pr1 |   1.0000 
         pr2 |  -0.1649   1.0000 
         pr3 |  -0.1033   0.0773   1.0000 
         pr4 |  -0.0596   0.0733  -0.0525   1.0000 
     pweeks1 |   0.9989  -0.1606  -0.1290  -0.0356   1.0000 
     pweeks2 |   0.1425  -0.9986  -0.0926  -0.0374   0.1396   1.0000 
     pweeks3 |  -0.1093   0.0744   0.9961  -0.0174  -0.1335  -0.0877   1.0000 
     pweeks4 |  -0.0533   0.0811  -0.0844   0.9967  -0.0284  -0.0455  -0.0475 

             |  pweeks4
-------------+---------
     pweeks4 |   1.0000 

.                 pwcorr pr1 weeks2

             |      pr1   weeks2
-------------+------------------
         pr1 |   1.0000 
      weeks2 |  -0.1849   1.0000 

.                 pwcorr pr2 weeks1

             |      pr2   weeks1
-------------+------------------
         pr2 |   1.0000 
      weeks1 |  -0.1957   1.0000 

.                 pwcorr pr3 weeks3

             |      pr3   weeks3
-------------+------------------
         pr3 |   1.0000 
      weeks3 |   0.9905   1.0000 

.                 drop pweeks* weeks1 weeks2 weeks3 weeks4        

.                 * Weeks comparison Ratings *
.                 factor $allvar1 $allvar2 $allvar3 $allvar4 $allvar5 $allvar6 $allvar
> 7 $allvar8 $allvar9 $allvar10 if persrat_1 ~=., factors(4)
(obs=2,562)
(collinear variables specified)

Factor analysis/correlation                      Number of obs    =      2,562
    Method: principal factors                    Retained factors =          4
    Rotation: (unrotated)                        Number of params =        330

    --------------------------------------------------------------------------
         Factor  |   Eigenvalue   Difference        Proportion   Cumulative
    -------------+------------------------------------------------------------
        Factor1  |     12.63157      6.02617            0.1775       0.1775
        Factor2  |      6.60540      1.37839            0.0928       0.2703
        Factor3  |      5.22701      1.28625            0.0734       0.3437
        Factor4  |      3.94076      0.73891            0.0554       0.3991
        Factor5  |      3.20185      0.30293            0.0450       0.4441
        Factor6  |      2.89892      0.32351            0.0407       0.4848
        Factor7  |      2.57541      0.32284            0.0362       0.5210
        Factor8  |      2.25257      0.17449            0.0317       0.5527
        Factor9  |      2.07808      0.08032            0.0292       0.5819
       Factor10  |      1.99775      0.15114            0.0281       0.6099
       Factor11  |      1.84661      0.05372            0.0259       0.6359
       Factor12  |      1.79289      0.10228            0.0252       0.6611
       Factor13  |      1.69061      0.14592            0.0238       0.6848
       Factor14  |      1.54469      0.06236            0.0217       0.7065
       Factor15  |      1.48233      0.09390            0.0208       0.7274
       Factor16  |      1.38842      0.09914            0.0195       0.7469
       Factor17  |      1.28928      0.04385            0.0181       0.7650
       Factor18  |      1.24544      0.12330            0.0175       0.7825
       Factor19  |      1.12214      0.01435            0.0158       0.7983
       Factor20  |      1.10779      0.02339            0.0156       0.8138
       Factor21  |      1.08441      0.07978            0.0152       0.8291
       Factor22  |      1.00462      0.05098            0.0141       0.8432
       Factor23  |      0.95364      0.07842            0.0134       0.8566
       Factor24  |      0.87522      0.00727            0.0123       0.8689
       Factor25  |      0.86795      0.06180            0.0122       0.8811
       Factor26  |      0.80615      0.02791            0.0113       0.8924
       Factor27  |      0.77824      0.03415            0.0109       0.9033
       Factor28  |      0.74408      0.07077            0.0105       0.9138
       Factor29  |      0.67331      0.03400            0.0095       0.9233
       Factor30  |      0.63931      0.04385            0.0090       0.9322
       Factor31  |      0.59546      0.03135            0.0084       0.9406
       Factor32  |      0.56412      0.03618            0.0079       0.9485
       Factor33  |      0.52794      0.07659            0.0074       0.9560
       Factor34  |      0.45134      0.01600            0.0063       0.9623
       Factor35  |      0.43534      0.03504            0.0061       0.9684
       Factor36  |      0.40030      0.01759            0.0056       0.9740
       Factor37  |      0.38271      0.01298            0.0054       0.9794
       Factor38  |      0.36973      0.02059            0.0052       0.9846
       Factor39  |      0.34914      0.02773            0.0049       0.9895
       Factor40  |      0.32141      0.03932            0.0045       0.9940
       Factor41  |      0.28209      0.02731            0.0040       0.9980
       Factor42  |      0.25477      0.06427            0.0036       1.0016
       Factor43  |      0.19050      0.02664            0.0027       1.0042
       Factor44  |      0.16386      0.01499            0.0023       1.0066
       Factor45  |      0.14887      0.01311            0.0021       1.0086
       Factor46  |      0.13575      0.01092            0.0019       1.0106
       Factor47  |      0.12483      0.01507            0.0018       1.0123
       Factor48  |      0.10976      0.01034            0.0015       1.0138
       Factor49  |      0.09942      0.03322            0.0014       1.0152
       Factor50  |      0.06620      0.00448            0.0009       1.0162
       Factor51  |      0.06172      0.00819            0.0009       1.0170
       Factor52  |      0.05353      0.00689            0.0008       1.0178
       Factor53  |      0.04664      0.01564            0.0007       1.0184
       Factor54  |      0.03100      0.00668            0.0004       1.0189
       Factor55  |      0.02432      0.00917            0.0003       1.0192
       Factor56  |      0.01515      0.00583            0.0002       1.0194
       Factor57  |      0.00932      0.00460            0.0001       1.0196
       Factor58  |      0.00472      0.00234            0.0001       1.0196
       Factor59  |      0.00238      0.00238            0.0000       1.0197
       Factor60  |      0.00000      0.00000            0.0000       1.0197
       Factor61  |      0.00000      0.00000            0.0000       1.0197
       Factor62  |      0.00000      0.00000            0.0000       1.0197
       Factor63  |     -0.00000      0.00000           -0.0000       1.0197
       Factor64  |     -0.00000      0.00000           -0.0000       1.0197
       Factor65  |     -0.00000      0.00000           -0.0000       1.0197
       Factor66  |     -0.00000      0.00907           -0.0000       1.0197
       Factor67  |     -0.00907      0.00307           -0.0001       1.0195
       Factor68  |     -0.01214      0.01342           -0.0002       1.0194
       Factor69  |     -0.02557      0.01205           -0.0004       1.0190
       Factor70  |     -0.03762      0.00520           -0.0005       1.0185
       Factor71  |     -0.04281      0.01295           -0.0006       1.0179
       Factor72  |     -0.05577      0.00526           -0.0008       1.0171
       Factor73  |     -0.06102      0.00302           -0.0009       1.0162
       Factor74  |     -0.06404      0.00623           -0.0009       1.0153
       Factor75  |     -0.07027      0.00992           -0.0010       1.0144
       Factor76  |     -0.08019      0.00383           -0.0011       1.0132
       Factor77  |     -0.08402      0.00482           -0.0012       1.0120
       Factor78  |     -0.08884      0.01102           -0.0012       1.0108
       Factor79  |     -0.09986      0.01063           -0.0014       1.0094
       Factor80  |     -0.11049      0.00335           -0.0016       1.0078
       Factor81  |     -0.11385      0.02569           -0.0016       1.0062
       Factor82  |     -0.13953      0.00491           -0.0020       1.0043
       Factor83  |     -0.14444      0.01594           -0.0020       1.0023
       Factor84  |     -0.16038            .           -0.0023       1.0000
    --------------------------------------------------------------------------
    LR test: independent vs. saturated: chi2(3486)=       . Prob>chi2 =      .

Factor loadings (pattern matrix) and unique variances

    ---------------------------------------------------------------------
        Variable |  Factor1   Factor2   Factor3   Factor4 |   Uniqueness 
    -------------+----------------------------------------+--------------
    partyrbrstmp |  -0.0048    0.5666    0.1829    0.1388 |      0.6262  
       militrank |  -0.7732    0.0401    0.1351    0.4105 |      0.2138  
     ldrrotation |  -0.2072   -0.1687    0.1026    0.2814 |      0.8389  
      milconsult |  -0.5536   -0.2957    0.0200    0.1676 |      0.5776  
    milmerit_mil |   0.1871   -0.5896    0.2072    0.1539 |      0.5507  
    milmeritpers |  -0.2006    0.5336   -0.2002   -0.0980 |      0.6253  
      milnotrial |  -0.2416    0.5173   -0.2186   -0.1066 |      0.6148  
      plebiscite |  -0.0508    0.2289    0.3447    0.1536 |      0.8026  
        heirclan |  -0.0444    0.3770   -0.2130   -0.1072 |      0.7991  
      officepers |  -0.1405    0.6147    0.0948   -0.2204 |      0.5448  
     paramilpers |  -0.1962    0.4827    0.1785   -0.2514 |      0.6334  
    ParamilParty |   0.3942   -0.1247    0.0319    0.1809 |      0.7953  
     ParamilFReb |  -0.0781   -0.1699   -0.2369    0.1079 |      0.8973  
    supportparty |   0.6902    0.5570    0.0276    0.3407 |      0.0965  
     partyleader |   0.5482    0.3742   -0.1222    0.0652 |      0.5403  
     localorgzns |   0.7030    0.4267   -0.0003    0.3643 |      0.1910  
       partymins |   0.6708    0.3012   -0.0378    0.2547 |      0.3930  
       excomcivn |   0.6978    0.2384    0.0034    0.2413 |      0.3980  
     multiethnic |   0.6222    0.0816    0.2342    0.1892 |      0.5155  
      monoethnic |  -0.1191    0.4025   -0.2530    0.0810 |      0.7533  
       heirparty |   0.7503   -0.1955   -0.0396    0.1288 |      0.3806  
      heirfamily |  -0.2820    0.4872   -0.0290   -0.2414 |      0.6240  
      legcompetn |   0.3825    0.1666    0.4032    0.2804 |      0.5847  
    leaderrela~s |  -0.0035    0.4159   -0.0617   -0.1515 |      0.8003  
       leaderciv |   0.7513   -0.2302    0.3473   -0.2003 |      0.2219  
       leadermil |  -0.8616    0.1077    0.1380    0.3436 |      0.1090  
     leaderrebel |   0.1876    0.1851   -0.7458   -0.2268 |      0.3228  
         heirciv |   0.7925   -0.3041    0.0559   -0.0354 |      0.2752  
          cabciv |   0.5346    0.0443    0.1046   -0.2277 |      0.6494  
          cabmil |  -0.5540   -0.0701   -0.1402    0.3086 |      0.5733  
      partymilit |   0.6027   -0.1319   -0.3838    0.0763 |      0.4662  
       ldrPriorD |   0.2307    0.0511    0.4609   -0.5159 |      0.4656  
        ldrParty |   0.5421   -0.3023    0.0990    0.2838 |      0.5244  
          ldrMil |  -0.8256   -0.0147    0.1372    0.1996 |      0.2595  
        ldrRebel |   0.1628    0.1213   -0.6940   -0.1716 |      0.4476  
          ldrCiv |   0.0810    0.0351    0.0948   -0.0108 |      0.9831  
        ldrOther |  -0.0196    0.2551    0.0431   -0.0612 |      0.9290  
        ldrForgn |   0.1950    0.0790   -0.0724   -0.0570 |      0.9473  
        ldrHered |  -0.0033    0.0013    0.0109   -0.1025 |      0.9894  
        SeizCoup |  -0.7624    0.0654    0.1803    0.1821 |      0.3488  
       SeizRebel |   0.3307   -0.0557   -0.6519    0.0283 |      0.4617  
       SeizUpris |  -0.0055   -0.1225    0.0330    0.1076 |      0.9723  
        SeizElec |   0.3222   -0.0102    0.5093   -0.4226 |      0.4581  
        SeizSucc |   0.0196    0.1023    0.0635    0.0323 |      0.9841  
         SeizFam |  -0.0027    0.0025    0.0149   -0.0870 |      0.9922  
     PartyhNoWin |   0.1378    0.1421   -0.1370    0.1333 |      0.9243  
       PartyhWin |   0.0453    0.0078    0.0829    0.1191 |      0.9768  
       PartyhReb |   0.3272   -0.0964   -0.5312    0.1032 |      0.5908  
    PartyhPrio~m |   0.3891    0.0118    0.4364   -0.2153 |      0.6117  
    PartyhNopa~y |  -0.6902   -0.5570   -0.0276   -0.3407 |      0.0965  
      PartyhElec |  -0.0061    0.1156    0.0853   -0.1019 |      0.9690  
    MilPartyAlly |  -0.1286    0.1182    0.0951    0.1637 |      0.9337  
      MilPartyNo |  -0.6825   -0.5299   -0.0114   -0.2641 |      0.1834  
    MilPartyPr~r |  -0.1339    0.1787    0.0519    0.4894 |      0.7080  
      nomilitary |   0.0931    0.0096    0.1429   -0.1531 |      0.9474  
    milethnic_~e |   0.1399   -0.2199   -0.0307    0.2713 |      0.8575  
    milethnic~ro |   0.0126    0.0432    0.0797   -0.3040 |      0.8992  
    milethnic~mo |  -0.2445    0.2522   -0.1066    0.0637 |      0.8612  
    sectyapp_p~y |   0.4783   -0.3833   -0.1836    0.1955 |      0.5524  
    sectyapppers |  -0.0750    0.6327    0.0979   -0.1532 |      0.5610  
    ElecldrPrD~t |   0.0243    0.0665    0.0390   -0.0840 |      0.9864  
    ElecldrPrDem |   0.0900    0.0182    0.2002   -0.3788 |      0.8080  
      ElecldrNot |  -0.2101   -0.3399   -0.4279   -0.1026 |      0.6467  
       Elecldr1C |   0.0463    0.2914    0.2083    0.1140 |      0.8566  
       Elecldr1F |   0.0640    0.1333   -0.1525   -0.0503 |      0.9524  
     ElecldrMLeg |   0.1509   -0.0275    0.1783   -0.0223 |      0.9442  
    ElecldrMExec |   0.0837    0.0401    0.2410    0.2173 |      0.8861  
    legnoms_in~t |  -0.0103   -0.0597   -0.3684   -0.0129 |      0.8604  
    legnoms_veto |   0.2105    0.2300    0.3634    0.2653 |      0.7003  
    legnoms_no~o |   0.0901   -0.1839    0.1372    0.0610 |      0.9355  
    legnoms_pr~m |   0.0502   -0.0302    0.1770   -0.2561 |      0.8997  
      LdrexHighR |  -0.6724   -0.0169    0.1270    0.3724 |      0.3928  
       LdrexLowR |  -0.3394    0.1482    0.0372   -0.0476 |      0.8592  
      LdrexRebel |   0.1918    0.1617   -0.7578   -0.2657 |      0.2921  
      LdrexDemEl |   0.2758    0.0419    0.4730   -0.5310 |      0.4165  
      LdrexParty |   0.6270   -0.3278    0.0590    0.2343 |      0.4410  
      LdrexLoyal |   0.0181    0.0686    0.0458   -0.0171 |      0.9926  
      LdrexReltv |  -0.0032    0.1688    0.0347   -0.0344 |      0.9691  
     LdrexRulFam |  -0.0146   -0.0159    0.0160   -0.1361 |      0.9808  
      LdrexOther |   0.0287    0.0297    0.0454    0.0032 |      0.9962  
    partye~mpers |   0.0093    0.6394   -0.0405    0.0942 |      0.5806  
    partyexcom~n |   0.3757   -0.1788   -0.0052    0.1169 |      0.8131  
    partyexcom~e |   0.2951   -0.1897    0.0287    0.0651 |      0.8719  
     createparty |  -0.2852    0.4856    0.0413    0.1376 |      0.6622  
    ---------------------------------------------------------------------

.                 rotate, promax(3)  

Factor analysis/correlation                      Number of obs    =      2,562
    Method: principal factors                    Retained factors =          4
    Rotation: oblique promax (Kaiser off)        Number of params =        330

    --------------------------------------------------------------------------
         Factor  |     Variance   Proportion    Rotated factors are correlated
    -------------+------------------------------------------------------------
        Factor1  |      9.63222       0.1353
        Factor2  |      8.77478       0.1233
        Factor3  |      7.66193       0.1077
        Factor4  |      5.56226       0.0782
    --------------------------------------------------------------------------
    LR test: independent vs. saturated: chi2(3486)=       . Prob>chi2 =      .

Rotated factor loadings (pattern matrix) and unique variances

    ---------------------------------------------------------------------
        Variable |  Factor1   Factor2   Factor3   Factor4 |   Uniqueness 
    -------------+----------------------------------------+--------------
    partyrbrstmp |   0.4416   -0.1310    0.4282    0.1765 |      0.6262  
       militrank |  -0.1319   -0.7422    0.1061    0.3377 |      0.2138  
     ldrrotation |  -0.0294   -0.3183   -0.2023    0.2135 |      0.8389  
      milconsult |  -0.3781   -0.4102   -0.1479    0.1556 |      0.5776  
    milmerit_mil |  -0.1442    0.0639   -0.6392    0.2591 |      0.5507  
    milmeritpers |   0.1395   -0.1137    0.5706   -0.2316 |      0.6253  
      milnotrial |   0.1002   -0.1308    0.5739   -0.2454 |      0.6148  
      plebiscite |   0.2277   -0.1010    0.1442    0.3663 |      0.8026  
        heirclan |   0.1246   -0.0121    0.3885   -0.2556 |      0.7991  
      officepers |   0.1562    0.0878    0.6728    0.0078 |      0.5448  
     paramilpers |   0.0285    0.1167    0.5901    0.0967 |      0.6334  
    ParamilParty |   0.2636    0.0684   -0.3164    0.0382 |      0.7953  
     ParamilFReb |  -0.0876   -0.1769   -0.1669   -0.1786 |      0.8973  
    supportparty |   0.9479    0.0244    0.1048   -0.0072 |      0.0965  
     partyleader |   0.5703    0.1719    0.1157   -0.1963 |      0.5403  
     localorgzns |   0.8899    0.0152   -0.0220   -0.0205 |      0.1910  
       partymins |   0.7221    0.0966   -0.0713   -0.0738 |      0.3930  
       excomcivn |   0.6921    0.1378   -0.1288   -0.0366 |      0.3980  
     multiethnic |   0.5314    0.2113   -0.2180    0.1933 |      0.5155  
      monoethnic |   0.2196   -0.2287    0.3524   -0.2356 |      0.7533  
       heirparty |   0.3816    0.2923   -0.4706   -0.0856 |      0.3806  
      heirfamily |  -0.0199    0.0133    0.6191   -0.0909 |      0.6240  
      legcompetn |   0.5171    0.0364   -0.1075    0.4063 |      0.5847  
    leaderrela~s |   0.1492    0.0805    0.4271   -0.1289 |      0.8003  
       leaderciv |   0.1641    0.6756   -0.3587    0.2023 |      0.2219  
       leadermil |  -0.1835   -0.7355    0.2227    0.3291 |      0.1090  
     leaderrebel |   0.0337    0.1077    0.2032   -0.8204 |      0.3228  
         heirciv |   0.2362    0.4907   -0.5065   -0.0355 |      0.2752  
          cabciv |   0.1811    0.5041   -0.0372   -0.0315 |      0.6494  
          cabmil |  -0.1564   -0.5914   -0.0140    0.0231 |      0.5733  
      partymilit |   0.2875    0.1739   -0.3420   -0.4188 |      0.4662  
       ldrPriorD |  -0.1559    0.6791    0.1924    0.2739 |      0.4656  
        ldrParty |   0.3084    0.0875   -0.5638    0.1244 |      0.5244  
          ldrMil |  -0.3323   -0.5794    0.1687    0.2931 |      0.2595  
        ldrRebel |   0.0196    0.0638    0.1316   -0.7478 |      0.4476  
          ldrCiv |   0.0639    0.0716    0.0079    0.0762 |      0.9831  
        ldrOther |   0.1063    0.0313    0.2532    0.0101 |      0.9290  
        ldrForgn |   0.1161    0.1295    0.0299   -0.1158 |      0.9473  
        ldrHered |  -0.0676    0.0907    0.0473   -0.0169 |      0.9894  
        SeizCoup |  -0.2576   -0.5274    0.2246    0.3165 |      0.3488  
       SeizRebel |   0.1382    0.0024   -0.1642   -0.6621 |      0.4617  
       SeizUpris |  -0.0057   -0.0790   -0.1515    0.0702 |      0.9723  
        SeizElec |  -0.0789    0.6624    0.0682    0.3386 |      0.4581  
        SeizSucc |   0.0973   -0.0120    0.0672    0.0607 |      0.9841  
         SeizFam |  -0.0562    0.0783    0.0412   -0.0090 |      0.9922  
     PartyhNoWin |   0.2441   -0.0887    0.0199   -0.1239 |      0.9243  
       PartyhWin |   0.1118   -0.0619   -0.0611    0.1063 |      0.9768  
       PartyhReb |   0.1662   -0.0334   -0.2320   -0.5216 |      0.5908  
    PartyhPrio~m |   0.1038    0.4967   -0.0254    0.3140 |      0.6117  
    PartyhNopa~y |  -0.9479   -0.0244   -0.1048    0.0072 |      0.0965  
      PartyhElec |   0.0044    0.0962    0.1459    0.0479 |      0.9690  
    MilPartyAlly |   0.1118   -0.1999    0.0715    0.1447 |      0.9337  
      MilPartyNo |  -0.8764   -0.0861   -0.1177    0.0408 |      0.1834  
    MilPartyPr~r |   0.3566   -0.5043   -0.0176    0.1875 |      0.7080  
      nomilitary |  -0.0356    0.2166    0.0442    0.0844 |      0.9474  
    milethnic_~e |   0.1201   -0.1523   -0.3547    0.0410 |      0.8575  
    milethnic~ro |  -0.1617    0.2886    0.1664   -0.0096 |      0.8992  
    milethnic~mo |   0.0539   -0.2326    0.2703   -0.0722 |      0.8612  
    sectyapp_p~y |   0.1525    0.0721   -0.5720   -0.1595 |      0.5524  
    sectyapppers |   0.2478    0.0626    0.6373    0.0194 |      0.5610  
    ElecldrPrD~t |   0.0009    0.0900    0.0862    0.0074 |      0.9864  
    ElecldrPrDem |  -0.1771    0.4259    0.1515    0.0787 |      0.8080  
      ElecldrNot |  -0.4114   -0.0919   -0.1765   -0.3921 |      0.6467  
       Elecldr1C |   0.2876   -0.0522    0.1848    0.2067 |      0.8566  
       Elecldr1F |   0.0768    0.0307    0.1173   -0.1786 |      0.9524  
     ElecldrMLeg |   0.0612    0.1438   -0.0645    0.1493 |      0.9442  
    ElecldrMExec |   0.2245   -0.0936   -0.0901    0.2789 |      0.8861  
    legnoms_in~t |  -0.0679   -0.0753   -0.0400   -0.3549 |      0.8604  
    legnoms_veto |   0.4480   -0.0563    0.0104    0.3812 |      0.7003  
    legnoms_no~o |  -0.0154    0.0424   -0.2160    0.1506 |      0.9355  
    legnoms_pr~m |  -0.1495    0.2957    0.0689    0.0978 |      0.8997  
      LdrexHighR |  -0.1357   -0.6522    0.0407    0.3106 |      0.3928  
       LdrexLowR |  -0.1284   -0.1423    0.2596    0.0562 |      0.8592  
      LdrexRebel |  -0.0041    0.1433    0.1989   -0.8415 |      0.2921  
      LdrexDemEl |  -0.1457    0.7199    0.1763    0.2764 |      0.4165  
      LdrexParty |   0.3060    0.1689   -0.5915    0.0633 |      0.4410  
      LdrexLoyal |   0.0428    0.0294    0.0605    0.0327 |      0.9926  
      LdrexReltv |   0.0801    0.0220    0.1616    0.0131 |      0.9691  
     LdrexRulFam |  -0.1061    0.1170    0.0508   -0.0184 |      0.9808  
      LdrexOther |   0.0383    0.0205    0.0145    0.0392 |      0.9962  
    partye~mpers |   0.4541   -0.1431    0.5076   -0.0589 |      0.5806  
    partyexcom~n |   0.1768    0.1108   -0.3286   -0.0090 |      0.8131  
    partyexcom~e |   0.0929    0.1224   -0.2891    0.0208 |      0.8719  
     createparty |   0.2286   -0.3046    0.4516    0.0802 |      0.6622  
    ---------------------------------------------------------------------

Factor rotation matrix

    --------------------------------------------------
                 | Factor1  Factor2  Factor3  Factor4 
    -------------+------------------------------------
         Factor1 |  0.7604   0.7384  -0.4793  -0.2192 
         Factor2 |  0.0196   0.2675   0.0902   0.9673 
         Factor3 |  0.4997  -0.0330   0.8021  -0.0107 
         Factor4 |  0.4143  -0.6181  -0.3446   0.1270 
    --------------------------------------------------

.                 predict pweeks1 pweeks2 pweeks3 pweeks4 
(regression scoring assumed)

Scoring coefficients (method = regression; based on promax(3) rotated factors)

    ------------------------------------------------------
        Variable |  Factor1   Factor2   Factor3   Factor4 
    -------------+----------------------------------------
    partyrbrstmp |  0.01390   0.00016   0.03874   0.01158 
       militrank |  0.06896  -0.03782  -0.03081   0.00299 
     ldrrotation |  0.01123  -0.03175  -0.03193  -0.00351 
      milconsult | -0.00689  -0.02209  -0.03766  -0.00231 
    milmerit_mil | -0.04726  -0.00537  -0.13276   0.10612 
    milmeritpers | -0.00802  -0.01877   0.08347   0.00354 
      milnotrial |  0.00815   0.01170   0.02803   0.00624 
      plebiscite |  0.01871   0.00958   0.01352  -0.00364 
        heirclan |  0.03288   0.00720   0.02314   0.00443 
      officepers |  0.02734   0.02100   0.05858   0.00349 
     paramilpers | -0.00608   0.01560   0.06866   0.00863 
    ParamilParty |  0.03592  -0.01653  -0.02960   0.00741 
     ParamilFReb | -0.00581  -0.02170  -0.02083  -0.01368 
    supportparty |  0.73089  -0.03362   0.00406   0.05128 
     partyleader |  0.00882   0.01229   0.01707  -0.00149 
     localorgzns |  0.04585  -0.01392  -0.02718  -0.01766 
       partymins |  0.01106  -0.00411   0.00172  -0.00968 
       excomcivn |  0.01895  -0.02229  -0.00987  -0.00223 
     multiethnic |  0.00000   0.00000   0.00000   0.00000 
      monoethnic | -0.04601  -0.05533   0.06296  -0.11343 
       heirparty |  0.04255   0.03849  -0.10770  -0.02553 
      heirfamily |  0.00752   0.00791   0.05829   0.00172 
      legcompetn |  0.04324   0.01311  -0.01488   0.08790 
    leaderrela~s |  0.01799   0.01407   0.03860  -0.00993 
       leaderciv | -0.01834   0.40566  -0.06734  -0.09057 
       leadermil |  0.00000   0.00000   0.00000   0.00000 
     leaderrebel | -0.08774   0.20340   0.01814  -0.26808 
         heirciv |  0.03081   0.03169  -0.07447  -0.01686 
          cabciv |  0.03115   0.06938  -0.00477   0.01124 
          cabmil | -0.00371  -0.08810  -0.03010  -0.00599 
      partymilit |  0.00904  -0.01948  -0.02563  -0.03833 
       ldrPriorD |  0.00240   0.21036   0.03655   0.06437 
        ldrParty |  0.10704   0.09392  -0.07899  -0.02499 
          ldrMil |  0.00000   0.00000   0.00000   0.00000 
        ldrRebel |  0.07806   0.06710   0.01750  -0.20147 
          ldrCiv |  0.00202   0.02099   0.00982   0.01903 
        ldrOther |  0.03729   0.04878   0.04114  -0.00934 
        ldrForgn |  0.05507   0.08693   0.00047  -0.07020 
        ldrHered | -0.01475   0.02461   0.01172  -0.00492 
        SeizCoup | -0.04698  -0.02974   0.02566   0.02654 
       SeizRebel |  0.00237  -0.03542  -0.01917  -0.15036 
       SeizUpris |  0.00689  -0.00766  -0.01038   0.00084 
        SeizElec | -0.02283   0.04348   0.04926   0.03405 
        SeizSucc |  0.00526  -0.00960   0.01348   0.00639 
         SeizFam | -0.00775   0.00052  -0.00009  -0.00100 
     PartyhNoWin | -0.01111   0.01988   0.01208  -0.03250 
       PartyhWin | -0.02922   0.00442   0.00072   0.02708 
       PartyhReb | -0.03570   0.02110  -0.06983  -0.09518 
    PartyhPrio~m | -0.06399   0.12660   0.00557   0.06258 
    PartyhNopa~y |  0.00000   0.00000   0.00000   0.00000 
      PartyhElec | -0.06211   0.00493   0.02925   0.00418 
    MilPartyAlly | -0.01628   0.04219   0.01122  -0.01650 
      MilPartyNo | -0.02072   0.09077  -0.00637  -0.01191 
    MilPartyPr~r | -0.00052   0.00232  -0.02487  -0.00660 
      nomilitary | -0.03511   0.03493   0.02168   0.03573 
    milethnic_~e |  0.00000   0.00000   0.00000   0.00000 
    milethnic~ro | -0.05832   0.09977   0.10706  -0.02449 
    milethnic~mo | -0.00615   0.00299   0.08887  -0.02793 
    sectyapp_p~y |  0.01682  -0.01219  -0.06574  -0.04188 
    sectyapppers |  0.02162   0.01523   0.11122   0.00805 
    ElecldrPrD~t |  0.00033   0.01552   0.02405   0.01312 
    ElecldrPrDem | -0.02809   0.10708   0.06282   0.08220 
      ElecldrNot |  0.00000   0.00000   0.00000   0.00000 
       Elecldr1C |  0.09521   0.00400   0.07582   0.18902 
       Elecldr1F |  0.03590   0.00432   0.03307  -0.01387 
     ElecldrMLeg |  0.02879   0.01781   0.00803   0.08386 
    ElecldrMExec |  0.07505  -0.00142   0.01966   0.16832 
    legnoms_in~t | -0.01156   0.00394  -0.00675  -0.00769 
    legnoms_veto |  0.04598  -0.01821   0.01507   0.04458 
    legnoms_no~o | -0.00768   0.00728  -0.02100  -0.01426 
    legnoms_pr~m | -0.01286   0.00941   0.00527  -0.01144 
      LdrexHighR |  0.00000   0.00000   0.00000   0.00000 
       LdrexLowR |  0.01988   0.09639   0.02861  -0.04372 
      LdrexRebel |  0.06841   0.10332  -0.01318  -0.22263 
      LdrexDemEl |  0.00003   0.22536   0.04438   0.00758 
      LdrexParty |  0.09314   0.14712  -0.13898  -0.09601 
      LdrexLoyal | -0.00984   0.03963   0.02140   0.00320 
      LdrexReltv |  0.01072   0.05426   0.03172  -0.04553 
     LdrexRulFam |  0.00278   0.04026   0.02034   0.00392 
      LdrexOther |  0.02908   0.03496  -0.00951  -0.01644 
    partye~mpers |  0.03102  -0.02666   0.12164   0.01604 
    partyexcom~n | -0.03698  -0.00998  -0.02357   0.00408 
    partyexcom~e | -0.04369   0.00307  -0.01471   0.01344 
     createparty | -0.08262   0.08975   0.06426  -0.03204 
    ------------------------------------------------------


.                 factor $allvar1 $allvar2 $allvar3 $allvar4 $allvar5 $allvar6 $allvar
> 7 $allvar8 $allvar9 $allvar10 persrat_1 milrat_1, factors(4)                
(obs=2,429)
(collinear variables specified)

Factor analysis/correlation                      Number of obs    =      2,429
    Method: principal factors                    Retained factors =          4
    Rotation: (unrotated)                        Number of params =        338

    --------------------------------------------------------------------------
         Factor  |   Eigenvalue   Difference        Proportion   Cumulative
    -------------+------------------------------------------------------------
        Factor1  |     13.36864      6.31909            0.1819       0.1819
        Factor2  |      7.04956      1.74646            0.0959       0.2779
        Factor3  |      5.30310      1.22844            0.0722       0.3500
        Factor4  |      4.07466      0.79396            0.0554       0.4055
        Factor5  |      3.28070      0.35114            0.0446       0.4501
        Factor6  |      2.92956      0.31794            0.0399       0.4900
        Factor7  |      2.61162      0.28152            0.0355       0.5255
        Factor8  |      2.33010      0.19868            0.0317       0.5572
        Factor9  |      2.13142      0.08000            0.0290       0.5862
       Factor10  |      2.05142      0.11383            0.0279       0.6142
       Factor11  |      1.93759      0.09017            0.0264       0.6405
       Factor12  |      1.84742      0.14544            0.0251       0.6657
       Factor13  |      1.70198      0.19159            0.0232       0.6888
       Factor14  |      1.51039      0.01911            0.0206       0.7094
       Factor15  |      1.49129      0.17112            0.0203       0.7297
       Factor16  |      1.32016      0.02699            0.0180       0.7476
       Factor17  |      1.29317      0.08589            0.0176       0.7652
       Factor18  |      1.20729      0.01296            0.0164       0.7817
       Factor19  |      1.19433      0.07735            0.0163       0.7979
       Factor20  |      1.11698      0.04380            0.0152       0.8131
       Factor21  |      1.07318      0.09436            0.0146       0.8277
       Factor22  |      0.97882      0.01614            0.0133       0.8410
       Factor23  |      0.96269      0.05570            0.0131       0.8541
       Factor24  |      0.90699      0.02627            0.0123       0.8665
       Factor25  |      0.88072      0.06192            0.0120       0.8785
       Factor26  |      0.81880      0.02082            0.0111       0.8896
       Factor27  |      0.79798      0.03067            0.0109       0.9005
       Factor28  |      0.76732      0.06191            0.0104       0.9109
       Factor29  |      0.70541      0.04295            0.0096       0.9205
       Factor30  |      0.66246      0.05384            0.0090       0.9295
       Factor31  |      0.60861      0.04078            0.0083       0.9378
       Factor32  |      0.56783      0.05257            0.0077       0.9455
       Factor33  |      0.51526      0.01424            0.0070       0.9525
       Factor34  |      0.50102      0.06110            0.0068       0.9594
       Factor35  |      0.43992      0.00936            0.0060       0.9653
       Factor36  |      0.43056      0.03937            0.0059       0.9712
       Factor37  |      0.39120      0.00624            0.0053       0.9765
       Factor38  |      0.38496      0.04002            0.0052       0.9818
       Factor39  |      0.34494      0.01651            0.0047       0.9865
       Factor40  |      0.32843      0.04300            0.0045       0.9909
       Factor41  |      0.28543      0.02708            0.0039       0.9948
       Factor42  |      0.25835      0.05382            0.0035       0.9983
       Factor43  |      0.20454      0.00857            0.0028       1.0011
       Factor44  |      0.19596      0.03478            0.0027       1.0038
       Factor45  |      0.16118      0.01738            0.0022       1.0060
       Factor46  |      0.14380      0.00966            0.0020       1.0079
       Factor47  |      0.13415      0.01567            0.0018       1.0098
       Factor48  |      0.11848      0.00889            0.0016       1.0114
       Factor49  |      0.10959      0.02106            0.0015       1.0129
       Factor50  |      0.08852      0.01572            0.0012       1.0141
       Factor51  |      0.07280      0.00930            0.0010       1.0151
       Factor52  |      0.06350      0.00932            0.0009       1.0159
       Factor53  |      0.05418      0.00313            0.0007       1.0167
       Factor54  |      0.05105      0.00568            0.0007       1.0174
       Factor55  |      0.04537      0.00623            0.0006       1.0180
       Factor56  |      0.03914      0.02142            0.0005       1.0185
       Factor57  |      0.01772      0.00283            0.0002       1.0187
       Factor58  |      0.01488      0.00408            0.0002       1.0189
       Factor59  |      0.01081      0.00798            0.0001       1.0191
       Factor60  |      0.00283      0.00283            0.0000       1.0191
       Factor61  |      0.00000      0.00000            0.0000       1.0191
       Factor62  |      0.00000      0.00000            0.0000       1.0191
       Factor63  |      0.00000      0.00000            0.0000       1.0191
       Factor64  |      0.00000      0.00000            0.0000       1.0191
       Factor65  |      0.00000      0.00000            0.0000       1.0191
       Factor66  |     -0.00000      0.00000           -0.0000       1.0191
       Factor67  |     -0.00000      0.00407           -0.0000       1.0191
       Factor68  |     -0.00407      0.00670           -0.0001       1.0191
       Factor69  |     -0.01078      0.00858           -0.0001       1.0189
       Factor70  |     -0.01936      0.00651           -0.0003       1.0187
       Factor71  |     -0.02587      0.01683           -0.0004       1.0183
       Factor72  |     -0.04270      0.00492           -0.0006       1.0177
       Factor73  |     -0.04762      0.00407           -0.0006       1.0171
       Factor74  |     -0.05169      0.00572           -0.0007       1.0164
       Factor75  |     -0.05741      0.00882           -0.0008       1.0156
       Factor76  |     -0.06623      0.00387           -0.0009       1.0147
       Factor77  |     -0.07010      0.00447           -0.0010       1.0137
       Factor78  |     -0.07457      0.00990           -0.0010       1.0127
       Factor79  |     -0.08447      0.00858           -0.0011       1.0116
       Factor80  |     -0.09305      0.00938           -0.0013       1.0103
       Factor81  |     -0.10243      0.00501           -0.0014       1.0089
       Factor82  |     -0.10744      0.01078           -0.0015       1.0075
       Factor83  |     -0.11822      0.01215           -0.0016       1.0059
       Factor84  |     -0.13037      0.01220           -0.0018       1.0041
       Factor85  |     -0.14257      0.01454           -0.0019       1.0021
       Factor86  |     -0.15711            .           -0.0021       1.0000
    --------------------------------------------------------------------------
    LR test: independent vs. saturated: chi2(3655)=       . Prob>chi2 =      .

Factor loadings (pattern matrix) and unique variances

    ---------------------------------------------------------------------
        Variable |  Factor1   Factor2   Factor3   Factor4 |   Uniqueness 
    -------------+----------------------------------------+--------------
    partyrbrstmp |  -0.0186    0.5437    0.1797    0.1698 |      0.6429  
       militrank |  -0.7774    0.0113    0.1381    0.4223 |      0.1981  
     ldrrotation |  -0.2187   -0.1969    0.1081    0.2942 |      0.8152  
      milconsult |  -0.5758   -0.3138    0.0268    0.1653 |      0.5420  
    milmerit_mil |   0.1721   -0.6050    0.2181    0.1272 |      0.5406  
    milmeritpers |  -0.1821    0.5540   -0.2087   -0.0789 |      0.6102  
      milnotrial |  -0.2285    0.5285   -0.2276   -0.1030 |      0.6061  
      plebiscite |  -0.0597    0.2566    0.3553    0.1381 |      0.7853  
        heirclan |  -0.0348    0.3672   -0.1939   -0.0519 |      0.8237  
      officepers |  -0.1255    0.6290    0.0980   -0.2008 |      0.5387  
     paramilpers |  -0.1802    0.4952    0.1902   -0.2420 |      0.6275  
    ParamilParty |   0.3948   -0.1195    0.0338    0.1696 |      0.7999  
     ParamilFReb |  -0.0982   -0.1721   -0.2486    0.1141 |      0.8859  
    supportparty |   0.6758    0.5415    0.0290    0.3859 |      0.1003  
     partyleader |   0.5502    0.3643   -0.1274    0.0899 |      0.5402  
     localorgzns |   0.6866    0.4145    0.0018    0.4131 |      0.1861  
       partymins |   0.6576    0.3071   -0.0387    0.2958 |      0.3842  
       excomcivn |   0.6883    0.2203    0.0051    0.2776 |      0.4007  
     multiethnic |   0.6220    0.0574    0.2172    0.1880 |      0.5272  
      monoethnic |  -0.1395    0.4053   -0.2293    0.1163 |      0.7502  
       heirparty |   0.7486   -0.1798   -0.0402    0.1375 |      0.3867  
      heirfamily |  -0.2819    0.5195   -0.0282   -0.2531 |      0.5858  
      legcompetn |   0.3734    0.1620    0.4129    0.3019 |      0.5727  
    leaderrela~s |   0.0154    0.4195   -0.0534   -0.1189 |      0.8068  
       leaderciv |   0.7548   -0.2059    0.3449   -0.2195 |      0.2207  
       leadermil |  -0.8651    0.0728    0.1438    0.3552 |      0.0994  
     leaderrebel |   0.1801    0.1999   -0.7403   -0.2106 |      0.3352  
         heirciv |   0.8032   -0.2926    0.0498   -0.0431 |      0.2649  
          cabciv |   0.5445    0.0705    0.1169   -0.2184 |      0.6372  
          cabmil |  -0.5727   -0.0926   -0.1493    0.2930 |      0.5553  
      partymilit |   0.6060   -0.1236   -0.3948    0.0728 |      0.4563  
       ldrPriorD |   0.2273    0.0579    0.4690   -0.4790 |      0.4956  
        ldrParty |   0.5395   -0.2947    0.1005    0.2738 |      0.5371  
          ldrMil |  -0.8230   -0.0434    0.1391    0.1979 |      0.2623  
        ldrRebel |   0.1546    0.1378   -0.6882   -0.1624 |      0.4571  
          ldrCiv |   0.0793    0.0541    0.0953   -0.0319 |      0.9807  
        ldrOther |  -0.0306    0.2684    0.0511   -0.0571 |      0.9212  
        ldrForgn |   0.2000    0.0723   -0.0879   -0.0717 |      0.9419  
        ldrHered |  -0.0062    0.0023    0.0117   -0.0979 |      0.9902  
        SeizCoup |  -0.7763    0.0671    0.1803    0.1633 |      0.3336  
       SeizRebel |   0.3223   -0.0555   -0.6514    0.0446 |      0.4668  
       SeizUpris |  -0.0086   -0.1052    0.0337    0.0834 |      0.9808  
        SeizElec |   0.3181    0.0003    0.5248   -0.3938 |      0.4684  
        SeizSucc |   0.0492    0.0848    0.0844    0.0607 |      0.9796  
         SeizFam |  -0.0044    0.0055    0.0156   -0.0861 |      0.9923  
     PartyhNoWin |   0.1312    0.1605   -0.1435    0.1150 |      0.9232  
       PartyhWin |   0.0381    0.0189    0.0957    0.1240 |      0.9737  
       PartyhReb |   0.3177   -0.0924   -0.5255    0.1150 |      0.6012  
    PartyhPrio~m |   0.3935    0.0065    0.4484   -0.1852 |      0.6098  
    PartyhNopa~y |  -0.6758   -0.5415   -0.0290   -0.3859 |      0.1003  
      PartyhElec |  -0.0170    0.1277    0.0707   -0.1457 |      0.9572  
    MilPartyAlly |  -0.1307    0.0997    0.0974    0.1816 |      0.9305  
      MilPartyNo |  -0.6678   -0.5199   -0.0128   -0.3011 |      0.1929  
    MilPartyPr~r |  -0.1566    0.1558    0.0628    0.5151 |      0.6820  
      nomilitary |   0.0886    0.0111    0.1327   -0.1322 |      0.9569  
    milethnic_~e |   0.1435   -0.2230   -0.0662    0.2351 |      0.8700  
    milethnic~ro |   0.0245    0.0445    0.0999   -0.2969 |      0.8993  
    milethnic~mo |  -0.2613    0.2524   -0.0782    0.0978 |      0.8523  
    sectyapp_p~y |   0.4842   -0.3853   -0.1949    0.1827 |      0.5457  
    sectyapppers |  -0.0642    0.6405    0.1044   -0.1292 |      0.5581  
    ElecldrPrD~t |   0.0196    0.0689    0.0292   -0.0689 |      0.9893  
    ElecldrPrDem |   0.0914    0.0300    0.2065   -0.3650 |      0.8149  
      ElecldrNot |  -0.1851   -0.3589   -0.4355   -0.1110 |      0.6350  
       Elecldr1C |   0.0256    0.3272    0.2146    0.1105 |      0.8340  
       Elecldr1F |   0.0581    0.1454   -0.1568   -0.0582 |      0.9475  
     ElecldrMLeg |   0.1584   -0.0700    0.1860    0.0107 |      0.9353  
    ElecldrMExec |   0.0745    0.0292    0.2408    0.2146 |      0.8896  
    legnoms_in~t |  -0.0176   -0.0731   -0.3697   -0.0102 |      0.8576  
    legnoms_veto |   0.1983    0.2234    0.3767    0.2748 |      0.6934  
    legnoms_no~o |   0.1040   -0.2040    0.1306    0.0654 |      0.9262  
    legnoms_pr~m |   0.0510   -0.0285    0.1823   -0.2437 |      0.9040  
      LdrexHighR |  -0.6767   -0.0404    0.1263    0.3833 |      0.3776  
       LdrexLowR |  -0.3497    0.1310    0.0454   -0.0524 |      0.8557  
      LdrexRebel |   0.1846    0.1831   -0.7513   -0.2561 |      0.3024  
      LdrexDemEl |   0.2745    0.0508    0.4798   -0.5002 |      0.4417  
      LdrexParty |   0.6296   -0.3129    0.0552    0.2091 |      0.4590  
      LdrexLoyal |   0.0213    0.0744    0.0459   -0.0298 |      0.9910  
      LdrexReltv |   0.0044    0.1772    0.0415   -0.0292 |      0.9660  
     LdrexRulFam |  -0.0184   -0.0108    0.0167   -0.1366 |      0.9806  
      LdrexOther |   0.0158    0.0005    0.0047   -0.0070 |      0.9997  
    partye~mpers |  -0.0075    0.6229   -0.0458    0.1311 |      0.5927  
    partyexcom~n |   0.3635   -0.1689   -0.0053    0.1172 |      0.8256  
    partyexcom~e |   0.2942   -0.1936    0.0384    0.0680 |      0.8699  
     createparty |  -0.2973    0.4635    0.0399    0.1486 |      0.6731  
       persrat_1 |  -0.1686    0.6319   -0.0239   -0.2574 |      0.5054  
        milrat_1 |  -0.8550   -0.0095   -0.0198    0.1348 |      0.2503  
    ---------------------------------------------------------------------

.                 rotate, promax(3)  

Factor analysis/correlation                      Number of obs    =      2,429
    Method: principal factors                    Retained factors =          4
    Rotation: oblique promax (Kaiser off)        Number of params =        338

    --------------------------------------------------------------------------
         Factor  |     Variance   Proportion    Rotated factors are correlated
    -------------+------------------------------------------------------------
        Factor1  |     10.62751       0.1446
        Factor2  |      9.16123       0.1247
        Factor3  |      7.47785       0.1018
        Factor4  |      5.36072       0.0730
    --------------------------------------------------------------------------
    LR test: independent vs. saturated: chi2(3655)=       . Prob>chi2 =      .

Rotated factor loadings (pattern matrix) and unique variances

    ---------------------------------------------------------------------
        Variable |  Factor1   Factor2   Factor3   Factor4 |   Uniqueness 
    -------------+----------------------------------------+--------------
    partyrbrstmp |  -0.1786    0.4284    0.3969    0.1653 |      0.6429  
       militrank |  -0.8583   -0.0538    0.0006    0.2490 |      0.1981  
     ldrrotation |  -0.3632    0.0056   -0.2563    0.1766 |      0.8152  
      milconsult |  -0.4985   -0.3316   -0.2105    0.1120 |      0.5420  
    milmerit_mil |   0.0705   -0.1483   -0.6294    0.2645 |      0.5406  
    milmeritpers |  -0.1116    0.1509    0.5649   -0.2444 |      0.6102  
      milnotrial |  -0.1230    0.0955    0.5653   -0.2614 |      0.6061  
      plebiscite |  -0.1515    0.2351    0.1704    0.3592 |      0.7853  
        heirclan |  -0.0187    0.1389    0.3538   -0.2235 |      0.8237  
      officepers |   0.0339    0.1482    0.6705    0.0360 |      0.5387  
     paramilpers |   0.0439    0.0222    0.5858    0.1356 |      0.6275  
    ParamilParty |   0.1433    0.2509   -0.2792    0.0365 |      0.7999  
     ParamilFReb |  -0.1576   -0.0740   -0.1770   -0.2137 |      0.8859  
    supportparty |   0.1134    0.9136    0.1271   -0.0058 |      0.1003  
     partyleader |   0.2691    0.5284    0.1426   -0.1799 |      0.5402  
     localorgzns |   0.1069    0.8664    0.0014   -0.0212 |      0.1861  
       partymins |   0.1866    0.7043   -0.0300   -0.0677 |      0.3842  
       excomcivn |   0.2304    0.6600   -0.1048   -0.0226 |      0.4007  
     multiethnic |   0.2793    0.4835   -0.1906    0.1932 |      0.5272  
      monoethnic |  -0.2284    0.2305    0.3343   -0.2316 |      0.7502  
       heirparty |   0.4098    0.3612   -0.4019   -0.0628 |      0.3867  
      heirfamily |  -0.0290   -0.0340    0.6387   -0.0781 |      0.5858  
      legcompetn |   0.0219    0.5156   -0.0940    0.4141 |      0.5727  
    leaderrela~s |   0.0720    0.1513    0.4171   -0.0996 |      0.8068  
       leaderciv |   0.7198    0.1100   -0.2632    0.2751 |      0.2207  
       leadermil |  -0.8686   -0.1108    0.1066    0.2478 |      0.0994  
     leaderrebel |   0.2385    0.0030    0.2348   -0.7934 |      0.3352  
         heirciv |   0.6036    0.1976   -0.4299    0.0068 |      0.2649  
          cabciv |   0.5445    0.1500    0.0299    0.0435 |      0.6372  
          cabmil |  -0.6231   -0.1232   -0.0768   -0.0623 |      0.5553  
      partymilit |   0.3427    0.2560   -0.2851   -0.4189 |      0.4563  
       ldrPriorD |   0.5553   -0.1817    0.2144    0.3835 |      0.4956  
        ldrParty |   0.1747    0.3033   -0.5158    0.1187 |      0.5371  
          ldrMil |  -0.7065   -0.2705    0.0686    0.2272 |      0.2623  
        ldrRebel |   0.1899   -0.0058    0.1640   -0.7289 |      0.4571  
          ldrCiv |   0.0801    0.0506    0.0419    0.0813 |      0.9807  
        ldrOther |   0.0077    0.0959    0.2671    0.0268 |      0.9212  
        ldrForgn |   0.1832    0.0798    0.0485   -0.1166 |      0.9419  
        ldrHered |   0.0742   -0.0727    0.0490   -0.0011 |      0.9902  
        SeizCoup |  -0.6532   -0.2093    0.1692    0.2526 |      0.3336  
       SeizRebel |   0.1548    0.1213   -0.1416   -0.6630 |      0.4668  
       SeizUpris |  -0.0630    0.0001   -0.1286    0.0528 |      0.9808  
        SeizElec |   0.5561   -0.1043    0.1020    0.4480 |      0.4684  
        SeizSucc |  -0.0170    0.1203    0.0329    0.0829 |      0.9796  
         SeizFam |   0.0659   -0.0612    0.0459    0.0040 |      0.9923  
     PartyhNoWin |  -0.0214    0.2291    0.0555   -0.1484 |      0.9232  
       PartyhWin |  -0.0695    0.1251   -0.0515    0.1079 |      0.9737  
       PartyhReb |   0.1045    0.1571   -0.2066   -0.5251 |      0.6012  
    PartyhPrio~m |   0.4367    0.0841   -0.0075    0.3939 |      0.6098  
    PartyhNopa~y |  -0.1134   -0.9136   -0.1271    0.0058 |      0.1003  
      PartyhElec |   0.0988   -0.0398    0.1825    0.0433 |      0.9572  
    MilPartyAlly |  -0.2358    0.1306    0.0330    0.1238 |      0.9305  
      MilPartyNo |  -0.1764   -0.8347   -0.1499    0.0313 |      0.1929  
    MilPartyPr~r |  -0.5250    0.3914   -0.0670    0.1317 |      0.6820  
      nomilitary |   0.1714   -0.0402    0.0486    0.1076 |      0.9569  
    milethnic_~e |  -0.0770    0.1136   -0.3379   -0.0293 |      0.8700  
    milethnic~ro |   0.2550   -0.1749    0.1703    0.0551 |      0.8993  
    milethnic~mo |  -0.2777    0.0812    0.2382   -0.0638 |      0.8523  
    sectyapp_p~y |   0.2015    0.1428   -0.5362   -0.1777 |      0.5457  
    sectyapppers |   0.0180    0.2372    0.6322    0.0467 |      0.5581  
    ElecldrPrD~t |   0.0647   -0.0009    0.0871    0.0137 |      0.9893  
    ElecldrPrDem |   0.3611   -0.1944    0.1722    0.1486 |      0.8149  
      ElecldrNot |  -0.0329   -0.3956   -0.2118   -0.4108 |      0.6350  
       Elecldr1C |  -0.0837    0.2877    0.2252    0.2040 |      0.8340  
       Elecldr1F |   0.0674    0.0578    0.1409   -0.1785 |      0.9475  
     ElecldrMLeg |   0.1135    0.0561   -0.1059    0.1805 |      0.9353  
    ElecldrMExec |  -0.1105    0.2230   -0.0947    0.2615 |      0.8896  
    legnoms_in~t |  -0.0171   -0.0776   -0.0514   -0.3637 |      0.8576  
    legnoms_veto |  -0.0817    0.4430    0.0149    0.3826 |      0.6934  
    legnoms_no~o |   0.0399   -0.0081   -0.2343    0.1458 |      0.9262  
    legnoms_pr~m |   0.2400   -0.1593    0.0748    0.1476 |      0.9040  
      LdrexHighR |  -0.7557   -0.0630   -0.0506    0.2284 |      0.3776  
       LdrexLowR |  -0.2030   -0.1324    0.2229    0.0541 |      0.8557  
      LdrexRebel |   0.2784   -0.0381    0.2404   -0.8096 |      0.3024  
      LdrexDemEl |   0.6054   -0.1777    0.2065    0.3885 |      0.4417  
      LdrexParty |   0.2865    0.2870   -0.5230    0.0597 |      0.4590  
      LdrexLoyal |   0.0351    0.0325    0.0731    0.0351 |      0.9910  
      LdrexReltv |   0.0155    0.0818    0.1664    0.0248 |      0.9660  
     LdrexRulFam |   0.0980   -0.1140    0.0586    0.0005 |      0.9806  
      LdrexOther |   0.0165    0.0031   -0.0001    0.0025 |      0.9997  
    partye~mpers |  -0.1572    0.4369    0.4831   -0.0712 |      0.5927  
    partyexcom~n |   0.1655    0.1676   -0.2898   -0.0039 |      0.8256  
    partyexcom~e |   0.1616    0.0867   -0.2719    0.0399 |      0.8699  
     createparty |  -0.3520    0.2252    0.4059    0.0485 |      0.6731  
       persrat_1 |   0.0434    0.0807    0.7109   -0.0903 |      0.5054  
        milrat_1 |  -0.6883   -0.3223    0.1367    0.0602 |      0.2503  
    ---------------------------------------------------------------------

Factor rotation matrix

    --------------------------------------------------
                 | Factor1  Factor2  Factor3  Factor4 
    -------------+------------------------------------
         Factor1 |  0.8380   0.6876  -0.3771  -0.0842 
         Factor2 | -0.0479   0.4825   0.8348  -0.0175 
         Factor3 |  0.1276   0.0106   0.0840   0.9963 
         Factor4 | -0.5284   0.5425  -0.3923  -0.0075 
    --------------------------------------------------

.                 predict weeks1 weeks2 weeks3 weeks4     
(regression scoring assumed)

Scoring coefficients (method = regression; based on promax(3) rotated factors)

    ------------------------------------------------------
        Variable |  Factor1   Factor2   Factor3   Factor4 
    -------------+----------------------------------------
    partyrbrstmp |  0.00032   0.00741   0.03183   0.01149 
       militrank | -0.03716   0.06966  -0.05094  -0.00728 
     ldrrotation | -0.02866   0.01702  -0.03873  -0.00993 
      milconsult | -0.01874  -0.00556  -0.03672  -0.00741 
    milmerit_mil | -0.01027  -0.04297  -0.13478   0.10436 
    milmeritpers | -0.03031   0.00282   0.07353   0.00159 
      milnotrial |  0.01097   0.00516   0.03143   0.00795 
      plebiscite |  0.01358   0.00967   0.01549   0.00137 
        heirclan | -0.00055   0.03123   0.01978   0.00520 
      officepers |  0.01519   0.02199   0.05972   0.00705 
     paramilpers |  0.00795  -0.00853   0.06720   0.01251 
    ParamilParty | -0.01649   0.04163  -0.03288   0.00404 
     ParamilFReb | -0.01963   0.00202  -0.02273  -0.02224 
    supportparty | -0.03146   0.75112  -0.00740   0.04238 
     partyleader |  0.01516   0.00682   0.01417   0.00244 
     localorgzns | -0.00806   0.04947  -0.02299  -0.02233 
       partymins |  0.00567   0.00696   0.00186  -0.01462 
       excomcivn | -0.01300   0.02322  -0.01357  -0.00891 
     multiethnic |  0.00000   0.00000   0.00000   0.00000 
      monoethnic | -0.04685  -0.03959   0.06421  -0.11281 
       heirparty |  0.05633   0.03678  -0.07979  -0.02381 
      heirfamily |  0.00987   0.01002   0.07992  -0.00722 
      legcompetn |  0.01179   0.03895  -0.01293   0.08845 
    leaderrela~s |  0.00734   0.01442   0.03430  -0.00411 
       leaderciv |  0.39392  -0.08948  -0.00224  -0.02058 
       leadermil |  0.00000   0.00000   0.00000   0.00000 
     leaderrebel |  0.20819  -0.13199   0.04382  -0.21996 
         heirciv |  0.03647   0.03312  -0.07830  -0.01572 
          cabciv |  0.06257   0.02206   0.00625   0.02740 
          cabmil | -0.08192   0.00507  -0.03429  -0.02459 
      partymilit | -0.00061   0.00051  -0.03312  -0.04682 
       ldrPriorD |  0.18713  -0.03359   0.05034   0.09816 
        ldrParty |  0.11766   0.08462  -0.06607  -0.01090 
          ldrMil |  0.00000   0.00000   0.00000   0.00000 
        ldrRebel |  0.09630   0.04916   0.03583  -0.18238 
          ldrCiv |  0.02671  -0.00836   0.01635   0.02083 
        ldrOther |  0.04493   0.03255   0.04885  -0.00138 
        ldrForgn |  0.09847   0.02729   0.01009  -0.05687 
        ldrHered |  0.02029  -0.01901   0.01341   0.00039 
        SeizCoup | -0.02003  -0.05453   0.02694   0.00909 
       SeizRebel | -0.02420   0.00704  -0.03501  -0.15257 
       SeizUpris | -0.00012   0.00726  -0.00552  -0.00423 
        SeizElec |  0.01863  -0.02077   0.04631   0.05018 
        SeizSucc | -0.02128   0.00740   0.00390   0.01420 
         SeizFam |  0.00192  -0.00918  -0.00007  -0.00111 
     PartyhNoWin |  0.03364  -0.01968   0.01278  -0.03431 
       PartyhWin |  0.01037  -0.02430   0.00289   0.02382 
       PartyhReb |  0.03862  -0.03818  -0.05754  -0.09487 
    PartyhPrio~m |  0.11025  -0.07910   0.00258   0.07851 
    PartyhNopa~y |  0.00000   0.00000   0.00000   0.00000 
      PartyhElec |  0.01616  -0.07379   0.02657   0.00206 
    MilPartyAlly |  0.03480  -0.01808   0.01798  -0.00370 
      MilPartyNo |  0.08171  -0.03726   0.00885   0.00420 
    MilPartyPr~r |  0.00494   0.00195  -0.01784  -0.00636 
      nomilitary |  0.02080  -0.04006   0.01650   0.03967 
    milethnic_~e |  0.00000   0.00000   0.00000   0.00000 
    milethnic~ro |  0.07871  -0.07619   0.10490   0.01015 
    milethnic~mo | -0.00223  -0.01006   0.07968  -0.01674 
    sectyapp_p~y | -0.00452   0.01636  -0.06984  -0.03875 
    sectyapppers |  0.00113   0.02034   0.10576   0.01363 
    ElecldrPrD~t |  0.01078  -0.00371   0.02166   0.01230 
    ElecldrPrDem |  0.07976  -0.04247   0.06585   0.10262 
      ElecldrNot |  0.00000   0.00000   0.00000   0.00000 
       Elecldr1C | -0.01340   0.09810   0.08369   0.18230 
       Elecldr1F |  0.00210   0.03311   0.03984  -0.01388 
     ElecldrMLeg |  0.00099   0.02951  -0.00754   0.08656 
    ElecldrMExec | -0.01336   0.07371   0.01553   0.15703 
    legnoms_in~t |  0.00428  -0.01463  -0.00523  -0.00964 
    legnoms_veto | -0.02368   0.05126   0.00919   0.04199 
    legnoms_no~o |  0.01068  -0.00943  -0.02182  -0.01582 
    legnoms_pr~m |  0.00689  -0.01410   0.00423  -0.00841 
      LdrexHighR |  0.00000   0.00000   0.00000   0.00000 
       LdrexLowR |  0.08674  -0.00821   0.03358  -0.02075 
      LdrexRebel |  0.13954   0.04041   0.01706  -0.19391 
      LdrexDemEl |  0.19730  -0.03770   0.06479   0.05651 
      LdrexParty |  0.17553   0.05807  -0.10107  -0.06368 
      LdrexLoyal |  0.04517  -0.02487   0.02450   0.00934 
      LdrexReltv |  0.05623  -0.00675   0.03584  -0.02616 
     LdrexRulFam |  0.03164  -0.00177   0.02439   0.01319 
      LdrexOther |  0.02681   0.00142  -0.00802  -0.01342 
    partye~mpers | -0.03591   0.02574   0.11291   0.00950 
    partyexcom~n | -0.00564  -0.04346  -0.02320   0.00436 
    partyexcom~e |  0.00297  -0.04968  -0.01601   0.01677 
     createparty |  0.08160  -0.10132   0.06872  -0.01915 
       persrat_1 |  0.00299  -0.00347   0.07958   0.00237 
        milrat_1 | -0.03641  -0.06036  -0.00987   0.00398 
    ------------------------------------------------------


.                 pwcorr pr1 pr2 pr3 pr4 pweeks1 pweeks2 pweeks3 pweeks4

             |      pr1      pr2      pr3      pr4  pweeks1  pweeks2  pweeks3
-------------+---------------------------------------------------------------
         pr1 |   1.0000 
         pr2 |  -0.1649   1.0000 
         pr3 |  -0.1033   0.0773   1.0000 
         pr4 |  -0.0596   0.0733  -0.0525   1.0000 
     pweeks1 |   0.9770  -0.1642  -0.0993   0.0293   1.0000 
     pweeks2 |  -0.1258  -0.8881   0.0964  -0.2722  -0.1814   1.0000 
     pweeks3 |  -0.2902   0.0959   0.9620  -0.0877  -0.3159   0.1774   1.0000 
     pweeks4 |   0.0223   0.1275   0.0839  -0.9653  -0.0516   0.0791   0.1093 

             |  pweeks4
-------------+---------
     pweeks4 |   1.0000 

.                 pwcorr pr1 weeks2

             |      pr1   weeks2
-------------+------------------
         pr1 |   1.0000 
      weeks2 |   0.9538   1.0000 

.                 pwcorr pr2 weeks1

             |      pr2   weeks1
-------------+------------------
         pr2 |   1.0000 
      weeks1 |  -0.9564   1.0000 

.                 pwcorr pr3 weeks3

             |      pr3   weeks3
-------------+------------------
         pr3 |   1.0000 
      weeks3 |   0.9688   1.0000 

.                 drop pweeks* weeks1 weeks2 weeks3 weeks4        

.                 corrtex $d1 $d2 $d3 $var_party $var_mil,  file(corrf) replace   titl
> e(Correlations)
(note: file corrf.tex not found)


\begin{table}[htbp]\centering \caption{Correlations\label{corrtable}}
\begin{tabular}{l  c  c  c  c  c  c  c  c  c  c  c  c  c  c  c  c  c  c  c  c  c  c }\
> hline\hline
\multicolumn{1}{c}{Variables} &Dimension 1&Dimension 2&Dimension 3&Number of parties (
> Gandhi)&Number of parties (H\&T)&Number of parties (DPI)&Number of parties (Svolik)&
> One or more parties (Gandhi)&One or more parties (H\&T)&One or more parties (DPI)&On
> e of more parties (Svolik)&Leader associated w. party (Svolik)&Institutions (Gandhi)
> &Legislative competitive index (Svolik)&Legislative competitive index (DPI)&Military
>  regime (Gandhi)&Military regime (H\&T)&Military regime (DPI)&Military regime (Svoli
> k)&Corporate military (Svolik)&Personal military (Svolik)&Indirect military (Svolik)
> \\ \hline
Dimension 1&1.000\\
Dimension 2&-0.165&1.000\\
Dimension 3&-0.103&0.077&1.000\\
Number of parties (Gandhi)&0.328&-0.030&-0.089&1.000\\
Number of parties (H\&T)&0.199&0.033&-0.065&0.792&1.000\\
Number of parties (DPI)&0.419&-0.283&-0.047&0.556&0.713&1.000\\
Number of parties (Svolik)&0.375&-0.001&-0.108&0.769&0.888&0.600&1.000\\
One or more parties (Gandhi)&0.624&-0.110&-0.144&0.805&0.573&0.462&0.629&1.000\\
One or more parties (H\&T)&0.630&-0.292&-0.119&0.440&0.707&0.677&0.477&0.605&1.000\\
One or more parties (DPI)&0.517&-0.347&-0.049&0.355&0.146&0.847&0.378&0.463&0.688&1.00
> 0\\
One of more parties (Svolik)&0.662&-0.054&-0.178&0.597&0.586&0.494&0.791&0.820&0.662&0
> .516&1.000\\
Leader associated w. party (Svolik)&0.724&-0.210&-0.030&-0.124&-0.109&0.155&-0.210&0.2
> 61&0.360&0.281&0.302&1.000\\
Institutions (Gandhi)&0.461&-0.242&-0.059&0.741&0.751&0.730&0.633&0.609&0.660&0.595&0.
> 523&0.157&1.000\\
Legislative competitive index (Svolik)&0.301&-0.263&0.030&0.285&0.177&0.545&0.419&0.27
> 6&0.430&0.546&0.319&0.180&0.442&1.000\\
Legislative competitive index (DPI)&0.389&-0.280&-0.021&0.566&0.700&0.963&0.610&0.445&
> 0.646&0.793&0.468&0.139&0.737&0.565&1.000\\
Military regime (Gandhi)&-0.039&0.854&0.186&0.013&0.112&-0.143&0.014&-0.017&-0.106&-0.
> 202&-0.037&-0.091&-0.146&-0.201&-0.154&1.000\\
Military regime (H\&T)&-0.176&0.735&0.119&-0.042&-0.087&-0.241&-0.119&-0.072&-0.258&-0
> .271&-0.143&-0.201&-0.228&-0.275&-0.233&0.659&1.000\\
Military regime (DPI)&-0.028&0.799&0.284&0.005&0.044&-0.147&-0.069&-0.044&-0.131&-0.20
> 2&-0.116&-0.089&-0.114&-0.179&-0.154&0.879&0.633&1.000\\
Military regime (Svolik)&-0.095&0.846&0.176&-0.004&0.037&-0.222&0.010&-0.104&-0.267&-0
> .292&-0.069&-0.139&-0.188&-0.259&-0.224&0.862&0.701&0.785&1.000\\
Corporate military (Svolik)&-0.166&0.554&0.027&-0.063&0.073&-0.219&-0.039&-0.158&-0.28
> 3&-0.293&-0.110&-0.146&-0.221&-0.214&-0.240&0.484&0.529&0.434&0.537&1.000\\
Personal military (Svolik)&0.042&0.500&0.216&0.051&0.028&-0.065&0.051&0.011&-0.061&-0.
> 105&0.015&-0.041&-0.017&-0.113&-0.051&0.575&0.345&0.540&0.674&-0.183&1.000\\
Indirect military (Svolik)&-0.028&0.130&-0.066&-0.004&-0.106&-0.033&-0.013&0.001&-0.04
> 1&0.017&0.000&-0.026&-0.055&-0.040&-0.029&0.130&0.079&0.059&0.197&-0.053&-0.067&1.00
> 0\\
\hline \hline 
 \end{tabular}
\end{table}

 Output writted successfully in file : corrf.tex

.                 
.                 * H&T claim personalism can be measured as time in power for a leade
> r *
.                 twoway (scatter $d3 gwf_leader_duration if gwf_leader_duration<26,ti
> tle(Personalism and leader time, size(medium)) /*
>                 */ mcolor(gs14) ylab(-2 (1) 2,glcolor(gs16)) xscale(range(1 25))) (l
> owess pr3 gwf_leader_duration if gwf_leader_duration<26,  yline(0, lpattern(dash)) l
> color(blue) /*
>                 */ xtitle(Leader time in power) ytitle(Personalism score) legend(off
> ) scheme(lean2))
(note: scheme lean2 not found, using s2color)

.                   graph export "$dir/golden/PersLeadertime.pdf", as(pdf)  replace
(file /Users/lee/Dropbox/Datavers/golden/PersLeadertime.pdf written in PDF format)

.   * Correlation matrices *
.                 * compare party dimension *
.                 local var = "cg_gparties ht_parties dpi_parties sv_parties cg_party 
> ht_party dpi_party sv_party lparty cg_ginst sv_legindex dpi_liec"

.                 foreach v of local var {
  2.                         recode `v' (-66=.) (-77=.) (-88=.) (-99=.)
  3.                 }
(cg_gparties: 0 changes made)
(ht_parties: 0 changes made)
(dpi_parties: 0 changes made)
(sv_parties: 0 changes made)
(cg_party: 0 changes made)
(ht_party: 0 changes made)
(dpi_party: 0 changes made)
(sv_party: 0 changes made)
(lparty: 0 changes made)
(cg_ginstitutions: 0 changes made)
(sv_legindex: 0 changes made)
(dpi_liec: 0 changes made)

.                 * Construct correlation matrix
.                 matrix m = J(3,12,.)

.                 matrix list m

m[3,12]
     c1   c2   c3   c4   c5   c6   c7   c8   c9  c10  c11  c12
r1    .    .    .    .    .    .    .    .    .    .    .    .
r2    .    .    .    .    .    .    .    .    .    .    .    .
r3    .    .    .    .    .    .    .    .    .    .    .    .

.                 local dimensions = "pr1 pr2 pr3"

.                 local i=1

.                 foreach t of local dimensions {
  2.                         local j = 1
  3.                         local klass = "cg_gparties ht_parties dpi_parties sv_part
> ies cg_party ht_party dpi_party sv_party lparty cg_ginst sv_legindex dpi_liec"
  4.                         foreach k of local klass {
  5.                                 corr `t' `k'
  6.                                 matrix j = r(C)
  7.                                 local s = round(j[2,1],.01)
  8.                                 local f = abs(`s')
  9.                                 matrix m[`i',`j'] =`f'
 10.                                 local j= `j' + 1
 11.                         }
 12.                         local i = `i'+1
 13.                 }
(obs=4,386)

             |      pr1 cg_gpa~s
-------------+------------------
         pr1 |   1.0000
 cg_gparties |   0.3280   1.0000

(obs=2,085)

             |      pr1 ht_par~s
-------------+------------------
         pr1 |   1.0000
  ht_parties |   0.1990   1.0000

(obs=2,538)

             |      pr1 dpi_pa~s
-------------+------------------
         pr1 |   1.0000
 dpi_parties |   0.4195   1.0000

(obs=4,171)

             |      pr1 sv_par~s
-------------+------------------
         pr1 |   1.0000
  sv_parties |   0.3752   1.0000

(obs=4,386)

             |      pr1 cg_party
-------------+------------------
         pr1 |   1.0000
    cg_party |   0.6241   1.0000

(obs=2,857)

             |      pr1 ht_party
-------------+------------------
         pr1 |   1.0000
    ht_party |   0.6304   1.0000

(obs=2,538)

             |      pr1 dpi_pa~y
-------------+------------------
         pr1 |   1.0000
   dpi_party |   0.5171   1.0000

(obs=4,171)

             |      pr1 sv_party
-------------+------------------
         pr1 |   1.0000
    sv_party |   0.6618   1.0000

(obs=3,488)

             |      pr1   lparty
-------------+------------------
         pr1 |   1.0000
      lparty |   0.7240   1.0000

(obs=4,386)

             |      pr1 cg_gin~s
-------------+------------------
         pr1 |   1.0000
cg_ginstit~s |   0.4607   1.0000

(obs=4,591)

             |      pr1 sv_leg~x
-------------+------------------
         pr1 |   1.0000
 sv_legindex |   0.3015   1.0000

(obs=2,538)

             |      pr1 dpi_liec
-------------+------------------
         pr1 |   1.0000
    dpi_liec |   0.3885   1.0000

(obs=4,386)

             |      pr2 cg_gpa~s
-------------+------------------
         pr2 |   1.0000
 cg_gparties |  -0.0295   1.0000

(obs=2,085)

             |      pr2 ht_par~s
-------------+------------------
         pr2 |   1.0000
  ht_parties |   0.0325   1.0000

(obs=2,538)

             |      pr2 dpi_pa~s
-------------+------------------
         pr2 |   1.0000
 dpi_parties |  -0.2832   1.0000

(obs=4,171)

             |      pr2 sv_par~s
-------------+------------------
         pr2 |   1.0000
  sv_parties |  -0.0009   1.0000

(obs=4,386)

             |      pr2 cg_party
-------------+------------------
         pr2 |   1.0000
    cg_party |  -0.1098   1.0000

(obs=2,857)

             |      pr2 ht_party
-------------+------------------
         pr2 |   1.0000
    ht_party |  -0.2921   1.0000

(obs=2,538)

             |      pr2 dpi_pa~y
-------------+------------------
         pr2 |   1.0000
   dpi_party |  -0.3467   1.0000

(obs=4,171)

             |      pr2 sv_party
-------------+------------------
         pr2 |   1.0000
    sv_party |  -0.0541   1.0000

(obs=3,488)

             |      pr2   lparty
-------------+------------------
         pr2 |   1.0000
      lparty |  -0.2104   1.0000

(obs=4,386)

             |      pr2 cg_gin~s
-------------+------------------
         pr2 |   1.0000
cg_ginstit~s |  -0.2424   1.0000

(obs=4,591)

             |      pr2 sv_leg~x
-------------+------------------
         pr2 |   1.0000
 sv_legindex |  -0.2630   1.0000

(obs=2,538)

             |      pr2 dpi_liec
-------------+------------------
         pr2 |   1.0000
    dpi_liec |  -0.2804   1.0000

(obs=4,386)

             |      pr3 cg_gpa~s
-------------+------------------
         pr3 |   1.0000
 cg_gparties |  -0.0890   1.0000

(obs=2,085)

             |      pr3 ht_par~s
-------------+------------------
         pr3 |   1.0000
  ht_parties |  -0.0648   1.0000

(obs=2,538)

             |      pr3 dpi_pa~s
-------------+------------------
         pr3 |   1.0000
 dpi_parties |  -0.0467   1.0000

(obs=4,171)

             |      pr3 sv_par~s
-------------+------------------
         pr3 |   1.0000
  sv_parties |  -0.1081   1.0000

(obs=4,386)

             |      pr3 cg_party
-------------+------------------
         pr3 |   1.0000
    cg_party |  -0.1439   1.0000

(obs=2,857)

             |      pr3 ht_party
-------------+------------------
         pr3 |   1.0000
    ht_party |  -0.1186   1.0000

(obs=2,538)

             |      pr3 dpi_pa~y
-------------+------------------
         pr3 |   1.0000
   dpi_party |  -0.0491   1.0000

(obs=4,171)

             |      pr3 sv_party
-------------+------------------
         pr3 |   1.0000
    sv_party |  -0.1781   1.0000

(obs=3,488)

             |      pr3   lparty
-------------+------------------
         pr3 |   1.0000
      lparty |  -0.0302   1.0000

(obs=4,386)

             |      pr3 cg_gin~s
-------------+------------------
         pr3 |   1.0000
cg_ginstit~s |  -0.0588   1.0000

(obs=4,591)

             |      pr3 sv_leg~x
-------------+------------------
         pr3 |   1.0000
 sv_legindex |   0.0301   1.0000

(obs=2,538)

             |      pr3 dpi_liec
-------------+------------------
         pr3 |   1.0000
    dpi_liec |  -0.0211   1.0000


.                 matrix list m

m[3,12]
     c1   c2   c3   c4   c5   c6   c7   c8   c9  c10  c11  c12
r1  .33   .2  .42  .38  .62  .63  .52  .66  .72  .46   .3  .39
r2  .03  .03  .28    0  .11  .29  .35  .05  .21  .24  .26  .28
r3  .09  .06  .05  .11  .14  .12  .05  .18  .03  .06  .03  .02

.                 plotmatrix, m(m) c(yellow) legend(off)   freq  split(0(.01).99)  xsi
> ze(3) ysize(2) /*
>                 */ xlab(1 "Parties (CGV)" 2 "Parties (HT)" 3 "Parties (DPI)" 4 "Part
> ies (Svolik)" 5 "1 party (CGV)"/*
>                 */ 6 "1 party (HT)" 7 "1 party (DPI)" 8 "1 party (Svolik)" 9 "Defact
> o (CGV)" 10 "Instiutions (CGV)" 11 "Leg index (Svolik)" 12 "Leg comp (DPI)", angle(4
> 5)) /*
>                 */ ylab(0 "Party" -1 "Military" -2 "Personal") 
WARNING: Tested only for Stata version 15.1 and higher.
Your Stata version 14.2 is not officially supported.
SPLIT 0 .01 .02 .03 .04 .05 .06 .07 .08 .09 .1 .11 .12 .13 .14 .15 .16 .17 .18 .19 .2 
> .21 .22 .23 .24 .25 .26 .27 .28 .29 .3 .31 .32 .33 .34 .35 .36 .37 .38 .39 .4 .41 .4
> 2 .43 .44 .45 .46 .47 .48 .49 .5 .51 .52 .53 .54 .55 .56 .57 .58 .59 .6 .61 .62 .63 
> .64 .65 .66 .67 .68 .69 .7 .71 .72 .73 .74 .75 .76 .77 .78 .79 .8 .81 .82 .83 .84 .8
> 5 .86 .87 .88 .89 .9 .91 .92 .93 .94 .95 .96 .97 .98 .99

.                 graph export "$dir/golden/CorrParty.pdf",as(pdf) replace
(file /Users/lee/Dropbox/Datavers/golden/CorrParty.pdf written in PDF format)

.                 * compare military dimension *
.                 local var = "cg_mil ht_mil dpi_mil sv_military sv_mil_corp sv_mil_pe
> rs sv_mil_ind"

.                 foreach v of local var {
  2.                         recode `v' (-66=.) (-77=.) (-88=.) (-99=.)
  3.                 }
(cg_mil: 0 changes made)
(ht_military: 0 changes made)
(dpi_military: 0 changes made)
(sv_military: 0 changes made)
(sv_mil_corp: 0 changes made)
(sv_mil_pers: 0 changes made)
(sv_mil_indir: 0 changes made)

.                 * Construct correlation matrix
.                 matrix m = J(3,7,.)

.                 matrix list m

m[3,7]
    c1  c2  c3  c4  c5  c6  c7
r1   .   .   .   .   .   .   .
r2   .   .   .   .   .   .   .
r3   .   .   .   .   .   .   .

.                 local dimensions = "pr1 pr2 pr3"

.                 local i=1

.                 foreach t of local dimensions {
  2.                         local j = 1
  3.                         local klass = "cg_mil ht_mil dpi_mil sv_military sv_mil_c
> orp sv_mil_pers sv_mil_ind"
  4.                         foreach k of local klass {
  5.                                 corr `t' `k'
  6.                                 matrix j = r(C)
  7.                                 local s = round(j[2,1],.01)
  8.                                 local f = abs(`s')
  9.                                 matrix m[`i',`j'] =`f'
 10.                                 local j= `j' + 1
 11.                         }
 12.                         local i = `i'+1
 13.                 }
(obs=4,260)

             |      pr1   cg_mil
-------------+------------------
         pr1 |   1.0000
      cg_mil |  -0.0385   1.0000

(obs=2,857)

             |      pr1 ht_mil~y
-------------+------------------
         pr1 |   1.0000
 ht_military |  -0.1761   1.0000

(obs=2,541)

             |      pr1 dpi_mi~y
-------------+------------------
         pr1 |   1.0000
dpi_military |  -0.0275   1.0000

(obs=4,171)

             |      pr1 sv_mil~y
-------------+------------------
         pr1 |   1.0000
 sv_military |  -0.0953   1.0000

(obs=4,171)

             |      pr1 sv_mil~p
-------------+------------------
         pr1 |   1.0000
 sv_mil_corp |  -0.1663   1.0000

(obs=4,171)

             |      pr1 sv_mil~s
-------------+------------------
         pr1 |   1.0000
 sv_mil_pers |   0.0416   1.0000

(obs=4,171)

             |      pr1 sv_mil~r
-------------+------------------
         pr1 |   1.0000
sv_mil_indir |  -0.0285   1.0000

(obs=4,260)

             |      pr2   cg_mil
-------------+------------------
         pr2 |   1.0000
      cg_mil |   0.8544   1.0000

(obs=2,857)

             |      pr2 ht_mil~y
-------------+------------------
         pr2 |   1.0000
 ht_military |   0.7351   1.0000

(obs=2,541)

             |      pr2 dpi_mi~y
-------------+------------------
         pr2 |   1.0000
dpi_military |   0.7993   1.0000

(obs=4,171)

             |      pr2 sv_mil~y
-------------+------------------
         pr2 |   1.0000
 sv_military |   0.8455   1.0000

(obs=4,171)

             |      pr2 sv_mil~p
-------------+------------------
         pr2 |   1.0000
 sv_mil_corp |   0.5544   1.0000

(obs=4,171)

             |      pr2 sv_mil~s
-------------+------------------
         pr2 |   1.0000
 sv_mil_pers |   0.4997   1.0000

(obs=4,171)

             |      pr2 sv_mil~r
-------------+------------------
         pr2 |   1.0000
sv_mil_indir |   0.1298   1.0000

(obs=4,260)

             |      pr3   cg_mil
-------------+------------------
         pr3 |   1.0000
      cg_mil |   0.1859   1.0000

(obs=2,857)

             |      pr3 ht_mil~y
-------------+------------------
         pr3 |   1.0000
 ht_military |   0.1195   1.0000

(obs=2,541)

             |      pr3 dpi_mi~y
-------------+------------------
         pr3 |   1.0000
dpi_military |   0.2841   1.0000

(obs=4,171)

             |      pr3 sv_mil~y
-------------+------------------
         pr3 |   1.0000
 sv_military |   0.1756   1.0000

(obs=4,171)

             |      pr3 sv_mil~p
-------------+------------------
         pr3 |   1.0000
 sv_mil_corp |   0.0267   1.0000

(obs=4,171)

             |      pr3 sv_mil~s
-------------+------------------
         pr3 |   1.0000
 sv_mil_pers |   0.2164   1.0000

(obs=4,171)

             |      pr3 sv_mil~r
-------------+------------------
         pr3 |   1.0000
sv_mil_indir |  -0.0662   1.0000


.                 matrix list m

m[3,7]
     c1   c2   c3   c4   c5   c6   c7
r1  .04  .18  .03   .1  .17  .04  .03
r2  .85  .74   .8  .85  .55   .5  .13
r3  .19  .12  .28  .18  .03  .22  .07

.                 plotmatrix, m(m) c(yellow) legend(off)   freq  split(0(.01).99)  xsi
> ze(3) ysize(2) /*
>                 */ xlab(1 "Military (CGV)" 2 "Military (HT)" 3 "Military (DPI)" 4 "M
> ilitary (Svolik)" 5 "Corporate mil (Sv)" 6 "Pers mil (Sv)" 7 "Indirect mil (Sv)", an
> gle(45)) /*
>                 */ ylab(0 "Party" -1 "Military" -2 "Personal") 
WARNING: Tested only for Stata version 15.1 and higher.
Your Stata version 14.2 is not officially supported.
SPLIT 0 .01 .02 .03 .04 .05 .06 .07 .08 .09 .1 .11 .12 .13 .14 .15 .16 .17 .18 .19 .2 
> .21 .22 .23 .24 .25 .26 .27 .28 .29 .3 .31 .32 .33 .34 .35 .36 .37 .38 .39 .4 .41 .4
> 2 .43 .44 .45 .46 .47 .48 .49 .5 .51 .52 .53 .54 .55 .56 .57 .58 .59 .6 .61 .62 .63 
> .64 .65 .66 .67 .68 .69 .7 .71 .72 .73 .74 .75 .76 .77 .78 .79 .8 .81 .82 .83 .84 .8
> 5 .86 .87 .88 .89 .9 .91 .92 .93 .94 .95 .96 .97 .98 .99

.                 graph export "$dir/golden/CorrMil.pdf",as(pdf) replace
(file /Users/lee/Dropbox/Datavers/golden/CorrMil.pdf written in PDF format)

. 
. ******************************************************
. *********** VDem correlation matrices ****************
. ******************************************************
.                         use temp,clear

.                         matrix m =  J(3,10,.)

.                         matrix list m

m[3,10]
     c1   c2   c3   c4   c5   c6   c7   c8   c9  c10
r1    .    .    .    .    .    .    .    .    .    .
r2    .    .    .    .    .    .    .    .    .    .
r3    .    .    .    .    .    .    .    .    .    .

.                         local dimensions = "pr1 pr2 xirtpers8"

.                         local i=1

.                         foreach t of local dimensions {
  2.                                 local j = 1
  3.                                 local klass = "v2xps_party v2psorgs v2psprbrch v2
> psprlnks v2exrescon v2lginvstp v2lgotovst pr1 pr2 xirtpers8"
  4.                                 foreach k of local klass {
  5.                                         spearman `t' `k'
  6.                                         local rho = r(rho)
  7.                                         local s = round(`rho',.01)
  8.                                         local f =  (`s')
  9.                                         matrix m[`i',`j'] =`f'
 10.                                         local j= `j' + 1
 11.                                 }
 12.                                 local i = `i'+1
 13.                         }

 Number of obs =    3866
Spearman's rho =       0.3652

Test of Ho: pr1 and v2xps_party are independent
    Prob > |t| =       0.0000

 Number of obs =    4552
Spearman's rho =       0.3203

Test of Ho: pr1 and v2psorgs are independent
    Prob > |t| =       0.0000

 Number of obs =    4552
Spearman's rho =       0.3475

Test of Ho: pr1 and v2psprbrch are independent
    Prob > |t| =       0.0000

 Number of obs =    4539
Spearman's rho =       0.1489

Test of Ho: pr1 and v2psprlnks are independent
    Prob > |t| =       0.0000

 Number of obs =    4552
Spearman's rho =       0.1184

Test of Ho: pr1 and v2exrescon are independent
    Prob > |t| =       0.0000

 Number of obs =    3761
Spearman's rho =      -0.0781

Test of Ho: pr1 and v2lginvstp are independent
    Prob > |t| =       0.0000

 Number of obs =    3761
Spearman's rho =      -0.0003

Test of Ho: pr1 and v2lgotovst are independent
    Prob > |t| =       0.9847

 Number of obs =    4591
Spearman's rho =       1.0000

Test of Ho: pr1 and pr1 are independent
    Prob > |t| =       0.0000

 Number of obs =    4591
Spearman's rho =      -0.2335

Test of Ho: pr1 and pr2 are independent
    Prob > |t| =       0.0000

 Number of obs =    4591
Spearman's rho =       0.2224

Test of Ho: pr1 and xirtpers8 are independent
    Prob > |t| =       0.0000

 Number of obs =    3866
Spearman's rho =      -0.0743

Test of Ho: pr2 and v2xps_party are independent
    Prob > |t| =       0.0000

 Number of obs =    4552
Spearman's rho =      -0.0558

Test of Ho: pr2 and v2psorgs are independent
    Prob > |t| =       0.0002

 Number of obs =    4552
Spearman's rho =      -0.0271

Test of Ho: pr2 and v2psprbrch are independent
    Prob > |t| =       0.0672

 Number of obs =    4539
Spearman's rho =      -0.1812

Test of Ho: pr2 and v2psprlnks are independent
    Prob > |t| =       0.0000

 Number of obs =    4552
Spearman's rho =      -0.3916

Test of Ho: pr2 and v2exrescon are independent
    Prob > |t| =       0.0000

 Number of obs =    3761
Spearman's rho =      -0.3212

Test of Ho: pr2 and v2lginvstp are independent
    Prob > |t| =       0.0000

 Number of obs =    3761
Spearman's rho =      -0.2487

Test of Ho: pr2 and v2lgotovst are independent
    Prob > |t| =       0.0000

 Number of obs =    4591
Spearman's rho =      -0.2335

Test of Ho: pr2 and pr1 are independent
    Prob > |t| =       0.0000

 Number of obs =    4591
Spearman's rho =       1.0000

Test of Ho: pr2 and pr2 are independent
    Prob > |t| =       0.0000

 Number of obs =    4591
Spearman's rho =       0.1517

Test of Ho: pr2 and xirtpers8 are independent
    Prob > |t| =       0.0000

 Number of obs =    3866
Spearman's rho =      -0.2409

Test of Ho: xirtpers8 and v2xps_party are independent
    Prob > |t| =       0.0000

 Number of obs =    4552
Spearman's rho =      -0.1981

Test of Ho: xirtpers8 and v2psorgs are independent
    Prob > |t| =       0.0000

 Number of obs =    4552
Spearman's rho =      -0.1525

Test of Ho: xirtpers8 and v2psprbrch are independent
    Prob > |t| =       0.0000

 Number of obs =    4539
Spearman's rho =      -0.1865

Test of Ho: xirtpers8 and v2psprlnks are independent
    Prob > |t| =       0.0000

 Number of obs =    4552
Spearman's rho =      -0.2954

Test of Ho: xirtpers8 and v2exrescon are independent
    Prob > |t| =       0.0000

 Number of obs =    3761
Spearman's rho =      -0.1693

Test of Ho: xirtpers8 and v2lginvstp are independent
    Prob > |t| =       0.0000

 Number of obs =    3761
Spearman's rho =      -0.1908

Test of Ho: xirtpers8 and v2lgotovst are independent
    Prob > |t| =       0.0000

 Number of obs =    4591
Spearman's rho =       0.2224

Test of Ho: xirtpers8 and pr1 are independent
    Prob > |t| =       0.0000

 Number of obs =    4591
Spearman's rho =       0.1517

Test of Ho: xirtpers8 and pr2 are independent
    Prob > |t| =       0.0000

 Number of obs =    4591
Spearman's rho =       1.0000

Test of Ho: xirtpers8 and xirtpers8 are independent
    Prob > |t| =            .

.                         matrix list m

m[3,10]
      c1    c2    c3    c4    c5    c6    c7    c8    c9   c10
r1   .37   .32   .35   .15   .12  -.08     0     1  -.23   .22
r2  -.07  -.06  -.03  -.18  -.39  -.32  -.25  -.23     1   .15
r3  -.24   -.2  -.15  -.19   -.3  -.17  -.19   .22   .15     1

.                         plotmatrix, m(m) c(ltblue) legend(off) freq  split(0(.01)1) 
>  xsize(3) ysize(2) ///
>                         xlab(1 "Party institutionalization" 2 "Party organizations" 
> 3 "Party branches" 4 "Party linkages" ///
>                         5 "Exec respects const." 6 "Legis investigate exec"  ///
>                         7 "Other investigate exec"  8 "Party" 9 "Military" 10 "IRT-P
> ers"  ///
>                         , angle(45) labsize(small))  ylab(0 "Party" -1 "Military" -2
>  "IRT-Pers") 
WARNING: Tested only for Stata version 15.1 and higher.
Your Stata version 14.2 is not officially supported.
SPLIT 0 .01 .02 .03 .04 .05 .06 .07 .08 .09 .1 .11 .12 .13 .14 .15 .16 .17 .18 .19 .2 
> .21 .22 .23 .24 .25 .26 .27 .28 .29 .3 .31 .32 .33 .34 .35 .36 .37 .38 .39 .4 .41 .4
> 2 .43 .44 .45 .46 .47 .48 .49 .5 .51 .52 .53 .54 .55 .56 .57 .58 .59 .6 .61 .62 .63 
> .64 .65 .66 .67 .68 .69 .7 .71 .72 .73 .74 .75 .76 .77 .78 .79 .8 .81 .82 .83 .84 .8
> 5 .86 .87 .88 .89 .9 .91 .92 .93 .94 .95 .96 .97 .98 .99 1

.                         graph export "$dir/golden/CorrVDemComponents1.pdf",as(pdf) r
> eplace                      
(file /Users/lee/Dropbox/Datavers/golden/CorrVDemComponents1.pdf written in PDF format
> )

.                         twoway (kdensity   xirtpers8 if xirtpers8~=. & v2xps_party~=
> .,col(red)) (kdensity xirtpers8 if xirtpers8~=. ///
>                                 & v2xps_party==.,col(blue) legend(lab(1 "Missing dat
> a on party institutionalization") lab(2 "Not missing") ///
>                                 ring(1) pos(6)col(2)))

.                         
.                         use temp,clear

.                         gen v2HOSpath_mil = v2expathhs==4

.                         gen v2HOSpath_party = v2expathhs==2

.                         gen v2HOGpath_mil = v2expathhg==4

.                         gen v2HOGpath_party = v2expathhg==2

.                         matrix m =  J(3,10,.)

.                         matrix list m

m[3,10]
     c1   c2   c3   c4   c5   c6   c7   c8   c9  c10
r1    .    .    .    .    .    .    .    .    .    .
r2    .    .    .    .    .    .    .    .    .    .
r3    .    .    .    .    .    .    .    .    .    .

.                         local dimensions = "pr1 pr2 xirtpers8"

.                         local i=1

.                         foreach t of local dimensions {
  2.                                 local j = 1
  3.                                 local klass = "v2HOSpath_party v2HOSpath_mil v2ex
> rmhsol_2 v2exrmhsol_4 v2exctlhs_2 v2exctlhs_4 v2exctlhs_0 pr1 pr2 xirtpers8"
  4.                                 foreach k of local klass {
  5.                                         spearman `t' `k'
  6.                                         local rho = r(rho)
  7.                                         local s = round(`rho',.01)
  8.                                         local f =  (`s')
  9.                                         matrix m[`i',`j'] =`f'
 10.                                         local j= `j' + 1
 11.                                 }
 12.                                 local i = `i'+1
 13.                         }

 Number of obs =    4591
Spearman's rho =       0.1892

Test of Ho: pr1 and v2HOSpath_party are independent
    Prob > |t| =       0.0000

 Number of obs =    4591
Spearman's rho =      -0.5028

Test of Ho: pr1 and v2HOSpath_mil are independent
    Prob > |t| =       0.0000

 Number of obs =    4552
Spearman's rho =       0.4151

Test of Ho: pr1 and v2exrmhsol_2 are independent
    Prob > |t| =       0.0000

 Number of obs =    4552
Spearman's rho =      -0.1199

Test of Ho: pr1 and v2exrmhsol_4 are independent
    Prob > |t| =       0.0000

 Number of obs =    4552
Spearman's rho =       0.5257

Test of Ho: pr1 and v2exctlhs_2 are independent
    Prob > |t| =       0.0000

 Number of obs =    4552
Spearman's rho =      -0.2802

Test of Ho: pr1 and v2exctlhs_4 are independent
    Prob > |t| =       0.0000

 Number of obs =    4552
Spearman's rho =      -0.1037

Test of Ho: pr1 and v2exctlhs_0 are independent
    Prob > |t| =       0.0000

 Number of obs =    4591
Spearman's rho =       1.0000

Test of Ho: pr1 and pr1 are independent
    Prob > |t| =       0.0000

 Number of obs =    4591
Spearman's rho =      -0.2335

Test of Ho: pr1 and pr2 are independent
    Prob > |t| =       0.0000

 Number of obs =    4591
Spearman's rho =       0.2224

Test of Ho: pr1 and xirtpers8 are independent
    Prob > |t| =       0.0000

 Number of obs =    4591
Spearman's rho =       0.0287

Test of Ho: pr2 and v2HOSpath_party are independent
    Prob > |t| =       0.0516

 Number of obs =    4591
Spearman's rho =      -0.2729

Test of Ho: pr2 and v2HOSpath_mil are independent
    Prob > |t| =       0.0000

 Number of obs =    4552
Spearman's rho =      -0.0249

Test of Ho: pr2 and v2exrmhsol_2 are independent
    Prob > |t| =       0.0924

 Number of obs =    4552
Spearman's rho =       0.4166

Test of Ho: pr2 and v2exrmhsol_4 are independent
    Prob > |t| =       0.0000

 Number of obs =    4552
Spearman's rho =      -0.1362

Test of Ho: pr2 and v2exctlhs_2 are independent
    Prob > |t| =       0.0000

 Number of obs =    4552
Spearman's rho =       0.5189

Test of Ho: pr2 and v2exctlhs_4 are independent
    Prob > |t| =       0.0000

 Number of obs =    4552
Spearman's rho =      -0.0319

Test of Ho: pr2 and v2exctlhs_0 are independent
    Prob > |t| =       0.0311

 Number of obs =    4591
Spearman's rho =      -0.2335

Test of Ho: pr2 and pr1 are independent
    Prob > |t| =       0.0000

 Number of obs =    4591
Spearman's rho =       1.0000

Test of Ho: pr2 and pr2 are independent
    Prob > |t| =       0.0000

 Number of obs =    4591
Spearman's rho =       0.1517

Test of Ho: pr2 and xirtpers8 are independent
    Prob > |t| =       0.0000

 Number of obs =    4591
Spearman's rho =      -0.0102

Test of Ho: xirtpers8 and v2HOSpath_party are independent
    Prob > |t| =       0.4902

 Number of obs =    4591
Spearman's rho =      -0.1043

Test of Ho: xirtpers8 and v2HOSpath_mil are independent
    Prob > |t| =       0.0000

 Number of obs =    4552
Spearman's rho =      -0.2048

Test of Ho: xirtpers8 and v2exrmhsol_2 are independent
    Prob > |t| =       0.0000

 Number of obs =    4552
Spearman's rho =       0.1059

Test of Ho: xirtpers8 and v2exrmhsol_4 are independent
    Prob > |t| =       0.0000

 Number of obs =    4552
Spearman's rho =      -0.1403

Test of Ho: xirtpers8 and v2exctlhs_2 are independent
    Prob > |t| =       0.0000

 Number of obs =    4552
Spearman's rho =       0.0293

Test of Ho: xirtpers8 and v2exctlhs_4 are independent
    Prob > |t| =       0.0477

 Number of obs =    4552
Spearman's rho =       0.2921

Test of Ho: xirtpers8 and v2exctlhs_0 are independent
    Prob > |t| =       0.0000

 Number of obs =    4591
Spearman's rho =       0.2224

Test of Ho: xirtpers8 and pr1 are independent
    Prob > |t| =       0.0000

 Number of obs =    4591
Spearman's rho =       0.1517

Test of Ho: xirtpers8 and pr2 are independent
    Prob > |t| =       0.0000

 Number of obs =    4591
Spearman's rho =       1.0000

Test of Ho: xirtpers8 and xirtpers8 are independent
    Prob > |t| =            .

.                         matrix list m

m[3,10]
      c1    c2    c3    c4    c5    c6    c7    c8    c9   c10
r1   .19   -.5   .42  -.12   .53  -.28   -.1     1  -.23   .22
r2   .03  -.27  -.02   .42  -.14   .52  -.03  -.23     1   .15
r3  -.01   -.1   -.2   .11  -.14   .03   .29   .22   .15     1

.                         plotmatrix, m(m) c(yellow) legend(off) freq  split(0(.001)1)
>   xsize(3) ysize(2) ///
>                         xlab(1 "Exec party path" 2 "Exec military path" 3 "Exec remo
> ved by party" 4 "Exec removed by military" ///
>                         5 "Exec need party approval"  6 "Exec need military approval
> " 7 "Exec no approval" 8 "Party" ///
>                         9 "Military" 10  "IRT-Pers" ///
>                         , angle(45) labsize(small))  ylab(0 "Party" -1 "Military" -2
>  "IRT-Pers") 
WARNING: Tested only for Stata version 15.1 and higher.
Your Stata version 14.2 is not officially supported.
SPLIT 0 .001 .002 .003 .004 .005 .006 .007 .008 .009 .01 .011 .012 .013 .014 .015 .016
>  .017 .018 .019 .02 .021 .022 .023 .024 .025 .026 .027 .028 .029 .03 .031 .032 .033 
> .034 .035 .036 .037 .038 .039 .04 .041 .042 .043 .044 .045 .046 .047 .048 .049 .05 .
> 051 .052 .053 .054 .055 .056 .057 .058 .059 .06 .061 .062 .063 .064 .065 .066 .067 .
> 068 .069 .07 .071 .072 .073 .074 .075 .076 .077 .078 .079 .08 .081 .082 .083 .084 .0
> 85 .086 .087 .088 .089 .09 .091 .092 .093 .094 .095 .096 .097 .098 .099 .1 .101 .102
>  .103 .104 .105 .106 .107 .108 .109 .11 .111 .112 .113 .114 .115 .116 .117 .118 .119
>  .12 .121 .122 .123 .124 .125 .126 .127 .128 .129 .13 .131 .132 .133 .134 .135 .136 
> .137 .138 .139 .14 .141 .142 .143 .144 .145 .146 .147 .148 .149 .15 .151 .152 .153 .
> 154 .155 .156 .157 .158 .159 .16 .161 .162 .163 .164 .165 .166 .167 .168 .169 .17 .1
> 71 .172 .173 .174 .175 .176 .177 .178 .179 .18 .181 .182 .183 .184 .185 .186 .187 .1
> 88 .189 .19 .191 .192 .193 .194 .195 .196 .197 .198 .199 .2 .201 .202 .203 .204 .205
>  .206 .207 .208 .209 .21 .211 .212 .213 .214 .215 .216 .217 .218 .219 .22 .221 .222 
> .223 .224 .225 .226 .227 .228 .229 .23 .231 .232 .233 .234 .235 .236 .237 .238 .239 
> .24 .241 .242 .243 .244 .245 .246 .247 .248 .249 .25 .251 .252 .253 .254 .255 .256 .
> 257 .258 .259 .26 .261 .262 .263 .264 .265 .266 .267 .268 .269 .27 .271 .272 .273 .2
> 74 .275 .276 .277 .278 .279 .28 .281 .282 .283 .284 .285 .286 .287 .288 .289 .29 .29
> 1 .292 .293 .294 .295 .296 .297 .298 .299 .3 .301 .302 .303 .304 .305 .306 .307 .308
>  .309 .31 .311 .312 .313 .314 .315 .316 .317 .318 .319 .32 .321 .322 .323 .324 .325 
> .326 .327 .328 .329 .33 .331 .332 .333 .334 .335 .336 .337 .338 .339 .34 .341 .342 .
> 343 .344 .345 .346 .347 .348 .349 .35 .351 .352 .353 .354 .355 .356 .357 .358 .359 .
> 36 .361 .362 .363 .364 .365 .366 .367 .368 .369 .37 .371 .372 .373 .374 .375 .376 .3
> 77 .378 .379 .38 .381 .382 .383 .384 .385 .386 .387 .388 .389 .39 .391 .392 .393 .39
> 4 .395 .396 .397 .398 .399 .4 .401 .402 .403 .404 .405 .406 .407 .408 .409 .41 .411 
> .412 .413 .414 .415 .416 .417 .418 .419 .42 .421 .422 .423 .424 .425 .426 .427 .428 
> .429 .43 .431 .432 .433 .434 .435 .436 .437 .438 .439 .44 .441 .442 .443 .444 .445 .
> 446 .447 .448 .449 .45 .451 .452 .453 .454 .455 .456 .457 .458 .459 .46 .461 .462 .4
> 63 .464 .465 .466 .467 .468 .469 .47 .471 .472 .473 .474 .475 .476 .477 .478 .479 .4
> 8 .481 .482 .483 .484 .485 .486 .487 .488 .489 .49 .491 .492 .493 .494 .495 .496 .49
> 7 .498 .499 .5 .501 .502 .503 .504 .505 .506 .507 .508 .509 .51 .511 .512 .513 .514 
> .515 .516 .517 .518 .519 .52 .521 .522 .523 .524 .525 .526 .527 .528 .529 .53 .531 .
> 532 .533 .534 .535 .536 .537 .538 .539 .54 .541 .542 .543 .544 .545 .546 .547 .548 .
> 549 .55 .551 .552 .553 .554 .555 .556 .557 .558 .559 .56 .561 .562 .563 .564 .565 .5
> 66 .567 .568 .569 .57 .571 .572 .573 .574 .575 .576 .577 .578 .579 .58 .581 .582 .58
> 3 .584 .585 .586 .587 .588 .589 .59 .591 .592 .593 .594 .595 .596 .597 .598 .599 .6 
> .601 .602 .603 .604 .605 .606 .607 .608 .609 .61 .611 .612 .613 .614 .615 .616 .617 
> .618 .619 .62 .621 .622 .623 .624 .625 .626 .627 .628 .629 .63 .631 .632 .633 .634 .
> 635 .636 .637 .638 .639 .64 .641 .642 .643 .644 .645 .646 .647 .648 .649 .65 .651 .6
> 52 .653 .654 .655 .656 .657 .658 .659 .66 .661 .662 .663 .664 .665 .666 .667 .668 .6
> 69 .67 .671 .672 .673 .674 .675 .676 .677 .678 .679 .68 .681 .682 .683 .684 .685 .68
> 6 .687 .688 .689 .69 .691 .692 .693 .694 .695 .696 .697 .698 .699 .7 .701 .702 .703 
> .704 .705 .706 .707 .708 .709 .71 .711 .712 .713 .714 .715 .716 .717 .718 .719 .72 .
> 721 .722 .723 .724 .725 .726 .727 .728 .729 .73 .731 .732 .733 .734 .735 .736 .737 .
> 738 .739 .74 .741 .742 .743 .744 .745 .746 .747 .748 .749 .75 .751 .752 .753 .754 .7
> 55 .756 .757 .758 .759 .76 .761 .762 .763 .764 .765 .766 .767 .768 .769 .77 .771 .77
> 2 .773 .774 .775 .776 .777 .778 .779 .78 .781 .782 .783 .784 .785 .786 .787 .788 .78
> 9 .79 .791 .792 .793 .794 .795 .796 .797 .798 .799 .8 .801 .802 .803 .804 .805 .806 
> .807 .808 .809 .81 .811 .812 .813 .814 .815 .816 .817 .818 .819 .82 .821 .822 .823 .
> 824 .825 .826 .827 .828 .829 .83 .831 .832 .833 .834 .835 .836 .837 .838 .839 .84 .8
> 41 .842 .843 .844 .845 .846 .847 .848 .849 .85 .851 .852 .853 .854 .855 .856 .857 .8
> 58 .859 .86 .861 .862 .863 .864 .865 .866 .867 .868 .869 .87 .871 .872 .873 .874 .87
> 5 .876 .877 .878 .879 .88 .881 .882 .883 .884 .885 .886 .887 .888 .889 .89 .891 .892
>  .893 .894 .895 .896 .897 .898 .899 .9 .901 .902 .903 .904 .905 .906 .907 .908 .909 
> .91 .911 .912 .913 .914 .915 .916 .917 .918 .919 .92 .921 .922 .923 .924 .925 .926 .
> 927 .928 .929 .93 .931 .932 .933 .934 .935 .936 .937 .938 .939 .94 .941 .942 .943 .9
> 44 .945 .946 .947 .948 .949 .95 .951 .952 .953 .954 .955 .956 .957 .958 .959 .96 .96
> 1 .962 .963 .964 .965 .966 .967 .968 .969 .97 .971 .972 .973 .974 .975 .976 .977 .97
> 8 .979 .98 .981 .982 .983 .984 .985 .986 .987 .988 .989 .99 .991 .992 .993 .994 .995
>  .996 .997 .998 .999 1

.                         graph export "$dir/golden/CorrVDemComponents2.pdf",as(pdf) r
> eplace                      
(file /Users/lee/Dropbox/Datavers/golden/CorrVDemComponents2.pdf written in PDF format
> )

. 
. 
. 
. ************************************************
. *** Variance comparison with Weeks's ratings ***
. ************************************************
.         use temp,clear

.         local ids = "caseid leadid"     

.         foreach tid of local ids {
  2.                         use temp, clear
  3.                         global id = "`tid'"
  4.                         drop case*
  5.                         egen caseid = group(gwf_casename)
  6.                         drop if caseid==.
  7.                         gen xid = "$id"
  8.                         if xid=="leadid" {
  9.                                 drop leadid
 10.                                 egen leadid = group(gwf_leaderid)
 11.                                 drop if leadid==.
 12.                         }
 13.                         local vars = "pr1 pr2 pr3 ipers1 imil1 Personalist"
 14.                         foreach i of local vars {
 15.                                 qui xtset $id year
 16.                                 qui xtsum `i'
 17.                                 scalar sdb`i' = r(sd_b)
 18.                                 scalar sdw`i' = r(sd_w)
 19.                                 scalar vart`i'= sdb`i' + sdw`i'
 20.                                 scalar varr`i' = sdw`i' / vart`i'
 21.                                 scalar list varr`i'
 22.                          }               
 23.                          gen n =.
 24.                          gen totalvar = .
 25.                          gen ratio  = .
 26.                          gen type = ""
 27.                          
.                          local c = 1
 28.                          foreach i of local vars {
 29.                                 replace n = `c'
 30.                                 replace totalvar = vart`i' if n==_n
 31.                                 replace ratio = varr`i' if n==_n
 32.                                 replace type = "`i'" if  n==_n
 33.                                 local c = `c' + 1
 34.                          }
 35.                         gen t = ""
 36.                         replace t =  "Party dimension" if type=="pr1"
 37.                         replace t =  "Military dimension" if type=="pr2"
 38.                         replace t =  "Personal dimension" if type=="pr3"
 39.                         replace t =  "Weeks military" if type=="imil1"
 40.                         replace t =  "Weeks personal" if type=="ipers1"
 41.                         replace t =  "GWF personal" if type=="Personalist"
 42. 
.                          if xid=="caseid" {
 43.                                 twoway (scatter ratio total, mlabel(t) mlabpos(12
> ) xtitle("Total variance") ytitle("Within/Total") /*
>                           */ ylab(0(.05).35,glcol(gs15)) xlab(0 (.5) 1.5) yscale(ran
> ge(.35)) xscale(range(0 1.6))  title("By regime-case") saving(caseid, replace)) /*
>                           */ (scatter ratio total if type=="pr3" | type=="ipers1" | 
> type=="Personalist", mlabel(t) mlabpos(12) mlabcolor(blue) legend(off))
 44.                          }
 45.                          
.                          if xid=="leadid" {
 46.                                 twoway (scatter ratio total, mlabel(t) mlabpos(12
> ) xtitle("Total variance") ytitle("Within/Total") /*
>                           */ ylab(0(.05).35,glcol(gs15)) xlab(0 (.5) 1.5) yscale(ran
> ge(.35)) xscale(range(0 1.6))  title("By leader") saving(leadid, replace)) /*
>                           */ (scatter ratio total if type=="pr3" | type=="ipers1"  |
>  type=="Personalist", mlabel(t) mlabpos(12) mlabcolor(blue) legend(off))
 47.                          }
 48.                 }
(0 observations deleted)
   varrpr1 =  .27748675
   varrpr2 =  .18952974
   varrpr3 =  .33066178
varripers1 =   .1937038
 varrimil1 =  .22550594
varrPersonalist =          0
(4,591 missing values generated)
(4,591 missing values generated)
(4,591 missing values generated)
(4,591 missing values generated)
(4,591 real changes made)
(1 real change made)
(1 real change made)
variable type was str1 now str3
(1 real change made)
(4,591 real changes made)
(1 real change made)
(1 real change made)
(1 real change made)
(4,591 real changes made)
(1 real change made)
(1 real change made)
(1 real change made)
(4,591 real changes made)
(1 real change made)
(1 real change made)
variable type was str3 now str6
(1 real change made)
(4,591 real changes made)
(1 real change made)
(1 real change made)
(1 real change made)
(4,591 real changes made)
(1 real change made)
(1 real change made)
variable type was str6 now str11
(1 real change made)
(4,591 missing values generated)
variable t was str1 now str15
(1 real change made)
variable t was str15 now str18
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(note: file caseid.gph not found)
(file caseid.gph saved)
(0 observations deleted)
(0 observations deleted)
   varrpr1 =  .23693961
   varrpr2 =  .08646316
   varrpr3 =  .24353237
varripers1 =  .10020903
 varrimil1 =   .1913521
varrPersonalist =          0
(4,591 missing values generated)
(4,591 missing values generated)
(4,591 missing values generated)
(4,591 missing values generated)
(4,591 real changes made)
(1 real change made)
(1 real change made)
variable type was str1 now str3
(1 real change made)
(4,591 real changes made)
(1 real change made)
(1 real change made)
(1 real change made)
(4,591 real changes made)
(1 real change made)
(1 real change made)
(1 real change made)
(4,591 real changes made)
(1 real change made)
(1 real change made)
variable type was str3 now str6
(1 real change made)
(4,591 real changes made)
(1 real change made)
(1 real change made)
(1 real change made)
(4,591 real changes made)
(1 real change made)
(1 real change made)
variable type was str6 now str11
(1 real change made)
(4,591 missing values generated)
variable t was str1 now str15
(1 real change made)
variable t was str15 now str18
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(note: file leadid.gph not found)
(file leadid.gph saved)

.                         gr combine caseid.gph leadid.gph  , col(2) xsize(9)

.                         graph export "$dir/golden/Variance.pdf", as(pdf)   replace
(file /Users/lee/Dropbox/Datavers/golden/Variance.pdf written in PDF format)

.                         
.                         
.                         * Variance de-composition for personalism measures *
.         local ids = "caseid leadid"     

.         foreach tid of local ids {
  2.                         use temp, clear
  3.                         gen gwf = Personalist
  4.                         gen both = ipers1~=. & irtpers8~=.
  5.                         *keep if both==1
.                         global id = "`tid'"
  6.                         drop case*
  7.                         egen caseid = group(gwf_casename)
  8.                         drop if caseid==.
  9.                         gen xid = "$id"
 10.                         if xid=="leadid" {
 11.                                 drop leadid
 12.                                 egen leadid = group(gwf_leaderid)
 13.                                 drop if leadid==.
 14.                         }
 15.                         local vars = "irtpers8 irtpers10 irtpers11 pr3 ipers1 gwf
> "
 16.                         foreach i of local vars {
 17.                                 qui xtset $id year
 18.                                 qui xtsum `i'
 19.                                 scalar sdb`i' = r(sd_b)
 20.                                 scalar sdw`i' = r(sd_w)
 21.                                 scalar vart`i'= sdb`i' + sdw`i'
 22.                                 scalar varr`i' = sdw`i' / vart`i'
 23.                                 scalar list varr`i' sdw`i'
 24.                          }               
 25.                          gen n =.
 26.                          gen totalvar = .
 27.                          gen ratio  = .
 28.                          gen type = ""
 29.                          
.                          local c = 1
 30.                          foreach i of local vars {
 31.                                 replace n = `c'
 32.                                 replace totalvar = sdw`i' if n==_n
 33.                                 replace ratio = varr`i' if n==_n
 34.                                 replace type = "`i'" if  n==_n
 35.                                 local c = `c' + 1
 36.                          }
 37.                         gen t = ""
 38.                         replace t =  "EFA" if type=="pr3"
 39.                         replace t =  "Weeks" if type=="ipers1"
 40.                         replace t =  "IRT-8" if type=="irtpers8"
 41.                         replace t =  "IRT-10" if type=="irtpers10"
 42.                         replace t =  "IRT-11" if type=="irtpers11"
 43.                         replace t =  "GWF" if type=="gwf"
 44. 
.                 
.                          if xid=="caseid" {
 45.                                 twoway (scatter ratio total, mlabel(t) msym(oh) m
> labpos(3) mlabcolor(blue)  xtitle("Within variance") ytitle("Within/Total") /*
>                           */ ylab(0(.1).4,glcol(gs15)) xlab(0 (.1) .5) xscale(range(
> 0 .5)) legend(off)  title("By regime-case") saving(caseid, replace)) 
 46.                          }
 47.                          
.                          if xid=="leadid" {
 48.                                 twoway (scatter ratio total, mlabel(t) msym(oh) m
> labpos(3) mlabcolor(blue)  xtitle("Within variance") ytitle("") /*
>                           */ ylab(0(.1).4,glcol(gs15)) xlab(0 (.1) .5) xscale(range(
> 0 .5)) legend(off)  title("By leader") saving(leadid, replace)) 
 49.                          }
 50.                 }
(0 observations deleted)
varrirtpers8 =  .37596609
sdwirtpers8 =  .46633623
varrirtpers10 =  .33876684
sdwirtpers10 =  .41991069
varrirtpers11 =  .33153738
sdwirtpers11 =  .41129655
   varrpr3 =  .33066178
    sdwpr3 =  .40595812
varripers1 =   .1937038
 sdwipers1 =  .09777418
   varrgwf =          0
    sdwgwf =          0
(4,591 missing values generated)
(4,591 missing values generated)
(4,591 missing values generated)
(4,591 missing values generated)
(4,591 real changes made)
(1 real change made)
(1 real change made)
variable type was str1 now str8
(1 real change made)
(4,591 real changes made)
(1 real change made)
(1 real change made)
variable type was str8 now str9
(1 real change made)
(4,591 real changes made)
(1 real change made)
(1 real change made)
(1 real change made)
(4,591 real changes made)
(1 real change made)
(1 real change made)
(1 real change made)
(4,591 real changes made)
(1 real change made)
(1 real change made)
(1 real change made)
(4,591 real changes made)
(1 real change made)
(1 real change made)
(1 real change made)
(4,591 missing values generated)
variable t was str1 now str3
(1 real change made)
variable t was str3 now str5
(1 real change made)
(1 real change made)
variable t was str5 now str6
(1 real change made)
(1 real change made)
(1 real change made)
(file caseid.gph saved)
(0 observations deleted)
(0 observations deleted)
varrirtpers8 =  .31118909
sdwirtpers8 =  .35667779
varrirtpers10 =  .27881782
sdwirtpers10 =  .32296003
varrirtpers11 =  .27302454
sdwirtpers11 =  .31631993
   varrpr3 =  .24353237
    sdwpr3 =  .29510669
varripers1 =  .10020903
 sdwipers1 =  .04587305
   varrgwf =          0
    sdwgwf =          0
(4,591 missing values generated)
(4,591 missing values generated)
(4,591 missing values generated)
(4,591 missing values generated)
(4,591 real changes made)
(1 real change made)
(1 real change made)
variable type was str1 now str8
(1 real change made)
(4,591 real changes made)
(1 real change made)
(1 real change made)
variable type was str8 now str9
(1 real change made)
(4,591 real changes made)
(1 real change made)
(1 real change made)
(1 real change made)
(4,591 real changes made)
(1 real change made)
(1 real change made)
(1 real change made)
(4,591 real changes made)
(1 real change made)
(1 real change made)
(1 real change made)
(4,591 real changes made)
(1 real change made)
(1 real change made)
(1 real change made)
(4,591 missing values generated)
variable t was str1 now str3
(1 real change made)
variable t was str3 now str5
(1 real change made)
(1 real change made)
variable t was str5 now str6
(1 real change made)
(1 real change made)
(1 real change made)
(file leadid.gph saved)

.                         gr combine caseid.gph leadid.gph  , 

.                         graph export "$dir/golden/Variance-IRT.pdf", as(pdf)   repla
> ce
(file /Users/lee/Dropbox/Datavers/golden/Variance-IRT.pdf written in PDF format)

. 
. ***************************
. **** Reliability tests ****
. ***************************
.                         use temp,clear

.                         set seed 98970875

.                         gen corrA=.
(4,591 missing values generated)

.                         gen corrB=.
(4,591 missing values generated)

.                         gen item=""
(4,591 missing values generated)

.                         gen n=_n

. 
.                         * test-retest reliability is testing the same group at diffe
> rent times; the measure is construction to pick up changes over time in regimes
.                         * but we can think of time differently, as calendar time per
> iods: does the data different in measurement across different time periods
.                         * one way to test this is dividing the sample into two bins 
> by the median calendar year, which is 1980
.                         centile year, centile(50)

                                                       -- Binom. Interp. --
    Variable |       Obs  Percentile    Centile        [95% Conf. Interval]
-------------+-------------------------------------------------------------
        year |     4,591         50        1980            1979        1980

.                         irt 2pl sectyapppers officepers paramilpers milmeritpers mil
> notrial partyexcompers createparty partyrbr if year<=1980

Fitting fixed-effects model:

Iteration 0:   log likelihood = -11661.736  
Iteration 1:   log likelihood = -11640.757  
Iteration 2:   log likelihood = -11640.742  
Iteration 3:   log likelihood = -11640.742  

Fitting full model:

Iteration 0:   log likelihood = -10507.008  
Iteration 1:   log likelihood = -10171.411  
Iteration 2:   log likelihood = -10147.902  
Iteration 3:   log likelihood = -10147.219  
Iteration 4:   log likelihood = -10147.218  

Two-parameter logistic model                    Number of obs     =      2,384
Log likelihood = -10147.218
------------------------------------------------------------------------------
             |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
sectyapppers |
     Discrim |   1.558241   .0960758    16.22   0.000     1.369936    1.746546
        Diff |   .0689864   .0378287     1.82   0.068    -.0051566    .1431294
-------------+----------------------------------------------------------------
officepers   |
     Discrim |   2.648597   .1864977    14.20   0.000     2.283068    3.014126
        Diff |  -.2928558   .0317936    -9.21   0.000    -.3551702   -.2305414
-------------+----------------------------------------------------------------
paramilpers  |
     Discrim |   .9150988   .0705196    12.98   0.000     .7768828    1.053315
        Diff |    1.10576   .0848527    13.03   0.000     .9394518    1.272068
-------------+----------------------------------------------------------------
milmeritpers |
     Discrim |   1.252567   .0820429    15.27   0.000     1.091766    1.413368
        Diff |   .4578057   .0470944     9.72   0.000     .3655024     .550109
-------------+----------------------------------------------------------------
milnotrial   |
     Discrim |   1.389456   .0889307    15.62   0.000     1.215155    1.563757
        Diff |   .7165166   .0497367    14.41   0.000     .6190345    .8139987
-------------+----------------------------------------------------------------
partye~mpers |
     Discrim |   2.708921   .2156708    12.56   0.000     2.286213    3.131628
        Diff |   .6320272   .0355791    17.76   0.000     .5622934    .7017611
-------------+----------------------------------------------------------------
createparty  |
     Discrim |   .9089136   .0860245    10.57   0.000     .7403087    1.077518
        Diff |    2.42648   .1917314    12.66   0.000     2.050694    2.802267
-------------+----------------------------------------------------------------
partyrbrstmp |
     Discrim |   2.419874   .1824916    13.26   0.000     2.062197    2.777551
        Diff |   .6283023   .0369888    16.99   0.000     .5558055    .7007991
------------------------------------------------------------------------------

.                         predict irtpersA, latent
(option ebmeans assumed)
(using 7 quadrature points)

.                         irt 2pl sectyapppers officepers paramilpers milmeritpers mil
> notrial partyexcompers createparty partyrbr if year>1980

Fitting fixed-effects model:

Iteration 0:   log likelihood = -11062.946  
Iteration 1:   log likelihood = -11045.033  
Iteration 2:   log likelihood = -11045.022  
Iteration 3:   log likelihood = -11045.022  

Fitting full model:

Iteration 0:   log likelihood = -10145.375  
Iteration 1:   log likelihood = -9564.8129  
Iteration 2:   log likelihood = -9541.1269  
Iteration 3:   log likelihood = -9540.1286  
Iteration 4:   log likelihood = -9540.1231  
Iteration 5:   log likelihood = -9540.1231  

Two-parameter logistic model                    Number of obs     =      2,207
Log likelihood = -9540.1231
------------------------------------------------------------------------------
             |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
sectyapppers |
     Discrim |   2.083501   .1453505    14.33   0.000      1.79862    2.368383
        Diff |  -.7412226   .0435057   -17.04   0.000    -.8264922   -.6559529
-------------+----------------------------------------------------------------
officepers   |
     Discrim |   2.883463    .235198    12.26   0.000     2.422484    3.344443
        Diff |  -.5738193   .0358905   -15.99   0.000    -.6441634   -.5034753
-------------+----------------------------------------------------------------
paramilpers  |
     Discrim |   1.331824   .0914232    14.57   0.000     1.152638     1.51101
        Diff |   .3439891   .0454753     7.56   0.000      .254859    .4331191
-------------+----------------------------------------------------------------
milmeritpers |
     Discrim |   1.397169   .0900694    15.51   0.000     1.220636    1.573702
        Diff |   .1717141   .0421631     4.07   0.000     .0890759    .2543524
-------------+----------------------------------------------------------------
milnotrial   |
     Discrim |   1.741959    .114022    15.28   0.000      1.51848    1.965438
        Diff |   .3184155   .0389886     8.17   0.000     .2419993    .3948317
-------------+----------------------------------------------------------------
partye~mpers |
     Discrim |   1.665994   .1238352    13.45   0.000     1.423282    1.908707
        Diff |   .5907396   .0456104    12.95   0.000     .5013448    .6801344
-------------+----------------------------------------------------------------
createparty  |
     Discrim |   1.779929   .1268423    14.03   0.000     1.531322    2.028535
        Diff |   1.170673   .0598855    19.55   0.000       1.0533    1.288047
-------------+----------------------------------------------------------------
partyrbrstmp |
     Discrim |   1.729301   .1265303    13.67   0.000     1.481306    1.977296
        Diff |   .7311354   .0480936    15.20   0.000     .6368737    .8253971
------------------------------------------------------------------------------

.                         predict irtpersB, latent
(option ebmeans assumed)
(using 7 quadrature points)

.                         spearman irtpers8 irtpersA irtpersB 
(obs=4591)

             | irtpers8 irtpersA irtpersB
-------------+---------------------------
    irtpers8 |   1.0000 
    irtpersA |   0.9935   1.0000 
    irtpersB |   0.9934   0.9776   1.0000 

.                         mat a =r(Rho)

.                         local a=a[1,2]

.                         replace corrA=`a' if n==1
(1 real change made)

.                         mat b =r(Rho)

.                         local b=b[1,3]

.                         replace corrB=`b' if n==1
(1 real change made)

.                         drop irtpersA irtpersB

. 
.                         * split-half correlation helps establish internal consistenc
> y or reliability
.                         * there are two ways to potentially think about this
.                         * first we could divide the sample randomly by observations 
> (not items)
. 
.                         gen u1 = runiform()

.                         irt 2pl sectyapppers officepers paramilpers milmeritpers mil
> notrial partyexcompers createparty partyrbr if u1<=.5

Fitting fixed-effects model:

Iteration 0:   log likelihood = -11228.611  
Iteration 1:   log likelihood = -11209.998  
Iteration 2:   log likelihood =  -11209.99  
Iteration 3:   log likelihood =  -11209.99  

Fitting full model:

Iteration 0:   log likelihood = -10194.682  
Iteration 1:   log likelihood = -9801.9526  
Iteration 2:   log likelihood = -9773.2106  
Iteration 3:   log likelihood = -9772.1866  
Iteration 4:   log likelihood = -9772.1861  

Two-parameter logistic model                    Number of obs     =      2,250
Log likelihood = -9772.1861
------------------------------------------------------------------------------
             |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
sectyapppers |
     Discrim |   1.547992   .0998025    15.51   0.000     1.352382    1.743601
        Diff |  -.3446663   .0415512    -8.29   0.000    -.4261052   -.2632274
-------------+----------------------------------------------------------------
officepers   |
     Discrim |   2.827588    .214575    13.18   0.000     2.407029    3.248147
        Diff |  -.3983071   .0332464   -11.98   0.000    -.4634689   -.3331454
-------------+----------------------------------------------------------------
paramilpers  |
     Discrim |   1.000946   .0751522    13.32   0.000     .8536505    1.148242
        Diff |   .7695731    .066852    11.51   0.000     .6385456    .9006006
-------------+----------------------------------------------------------------
milmeritpers |
     Discrim |   1.340475   .0883624    15.17   0.000     1.167288    1.513662
        Diff |   .2977157   .0439557     6.77   0.000     .2115641    .3838673
-------------+----------------------------------------------------------------
milnotrial   |
     Discrim |   1.408884   .0934654    15.07   0.000     1.225696    1.592073
        Diff |   .5479634   .0470245    11.65   0.000      .455797    .6401298
-------------+----------------------------------------------------------------
partye~mpers |
     Discrim |   2.343469   .1908185    12.28   0.000     1.969471    2.717466
        Diff |   .5938808   .0384157    15.46   0.000     .5185875    .6691741
-------------+----------------------------------------------------------------
createparty  |
     Discrim |   1.221231   .0966769    12.63   0.000     1.031748    1.410714
        Diff |   1.728916   .1063484    16.26   0.000     1.520477    1.937355
-------------+----------------------------------------------------------------
partyrbrstmp |
     Discrim |    2.39301    .197593    12.11   0.000     2.005735    2.780286
        Diff |   .6513816   .0393218    16.57   0.000     .5743124    .7284509
------------------------------------------------------------------------------

.                         predict irtpersA, latent
(option ebmeans assumed)
(using 7 quadrature points)

.                         irt 2pl sectyapppers officepers paramilpers milmeritpers mil
> notrial partyexcompers createparty partyrbr if u1>.5

Fitting fixed-effects model:

Iteration 0:   log likelihood = -11739.097  
Iteration 1:   log likelihood = -11719.579  
Iteration 2:   log likelihood = -11719.571  
Iteration 3:   log likelihood = -11719.571  

Fitting full model:

Iteration 0:   log likelihood = -10585.058  
Iteration 1:   log likelihood = -10148.032  
Iteration 2:   log likelihood = -10122.307  
Iteration 3:   log likelihood = -10121.346  
Iteration 4:   log likelihood = -10121.344  
Iteration 5:   log likelihood = -10121.344  

Two-parameter logistic model                    Number of obs     =      2,341
Log likelihood = -10121.344
------------------------------------------------------------------------------
             |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
sectyapppers |
     Discrim |   1.980008   .1236466    16.01   0.000     1.737665     2.22235
        Diff |  -.3242684   .0358398    -9.05   0.000    -.3945131   -.2540237
-------------+----------------------------------------------------------------
officepers   |
     Discrim |   2.893618   .2117103    13.67   0.000     2.478674    3.308563
        Diff |  -.4433421   .0326692   -13.57   0.000    -.5073725   -.3793117
-------------+----------------------------------------------------------------
paramilpers  |
     Discrim |   1.216484   .0817107    14.89   0.000     1.056334    1.376634
        Diff |   .6044808   .0519568    11.63   0.000     .5026472    .7063144
-------------+----------------------------------------------------------------
milmeritpers |
     Discrim |   1.383467   .0869667    15.91   0.000     1.213016    1.553919
        Diff |   .3217388   .0426023     7.55   0.000     .2382398    .4052378
-------------+----------------------------------------------------------------
milnotrial   |
     Discrim |   1.710824   .1070193    15.99   0.000      1.50107    1.920578
        Diff |   .4774896   .0401154    11.90   0.000     .3988649    .5561143
-------------+----------------------------------------------------------------
partye~mpers |
     Discrim |   1.995752   .1418537    14.07   0.000     1.717724     2.27378
        Diff |   .6188211   .0403687    15.33   0.000     .5396999    .6979422
-------------+----------------------------------------------------------------
createparty  |
     Discrim |   1.337013   .0981771    13.62   0.000      1.14459    1.529437
        Diff |   1.575432   .0881104    17.88   0.000     1.402739    1.748125
-------------+----------------------------------------------------------------
partyrbrstmp |
     Discrim |   1.752232     .12163    14.41   0.000     1.513841    1.990622
        Diff |   .7021985   .0448751    15.65   0.000     .6142449    .7901521
------------------------------------------------------------------------------

.                         predict irtpersB, latent
(option ebmeans assumed)
(using 7 quadrature points)

.                         spearman irtpers8 irtpersA irtpersB 
(obs=4591)

             | irtpers8 irtpersA irtpersB
-------------+---------------------------
    irtpers8 |   1.0000 
    irtpersA |   0.9981   1.0000 
    irtpersB |   0.9981   0.9948   1.0000 

.                         mat a =r(Rho)

.                         local a=a[1,2]

.                         replace corrA=`a' if n==2
(1 real change made)

.                         mat b =r(Rho)

.                         local b=b[1,3]

.                         replace corrB=`b' if n==2
(1 real change made)

.                         drop irtpersA irtpersB

. 
.                         * or we could do split-half correlation by item, which is th
> e more standard way to do this
.                         * in this application, there are probably two subdimensions 
> to personalism in the data (see the Appendix)
.                         * if we divide the eight items simply along these two subdim
> ensions, we will get not get an appropriate
.                         * split-half correlation test, which assumes all items belon
> g on the same dimensions
.                         * instead we split the items into two groups such that each 
> group has two items from each subdimension (party or security)
. 
.                         irt 2pl sectyapppers milmeritpers createparty partyrbr 

Fitting fixed-effects model:

Iteration 0:   log likelihood = -11087.076  
Iteration 1:   log likelihood = -11073.976  
Iteration 2:   log likelihood =  -11073.97  
Iteration 3:   log likelihood =  -11073.97  

Fitting full model:

Iteration 0:   log likelihood = -10663.389  
Iteration 1:   log likelihood = -10528.458  
Iteration 2:   log likelihood =  -10516.83  
Iteration 3:   log likelihood = -10516.706  
Iteration 4:   log likelihood = -10516.706  

Two-parameter logistic model                    Number of obs     =      4,591
Log likelihood = -10516.706
------------------------------------------------------------------------------
             |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
sectyapppers |
     Discrim |   1.694467   .1263421    13.41   0.000     1.446841    1.942093
        Diff |  -.3445237    .029297   -11.76   0.000    -.4019447   -.2871027
-------------+----------------------------------------------------------------
milmeritpers |
     Discrim |   1.126588   .0713508    15.79   0.000     .9867428    1.266433
        Diff |   .3450765   .0362351     9.52   0.000      .274057    .4160961
-------------+----------------------------------------------------------------
createparty  |
     Discrim |   1.437147   .1034913    13.89   0.000     1.234308    1.639987
        Diff |   1.538756   .0731943    21.02   0.000     1.395298    1.682215
-------------+----------------------------------------------------------------
partyrbrstmp |
     Discrim |   1.278389    .084345    15.16   0.000     1.113076    1.443703
        Diff |   .8512538    .047261    18.01   0.000     .7586239    .9438837
------------------------------------------------------------------------------

.                         predict irtpersA, latent
(option ebmeans assumed)
(using 7 quadrature points)

.                         irt 2pl officepers paramilpers milnotrial partyexcompers 

Fitting fixed-effects model:

Iteration 0:   log likelihood = -11882.529  
Iteration 1:   log likelihood = -11857.495  
Iteration 2:   log likelihood = -11857.485  
Iteration 3:   log likelihood = -11857.485  

Fitting full model:

Iteration 0:   log likelihood = -11358.785  
Iteration 1:   log likelihood = -10987.337  
Iteration 2:   log likelihood = -10962.767  
Iteration 3:   log likelihood = -10956.243  
Iteration 4:   log likelihood = -10955.179  
Iteration 5:   log likelihood = -10954.927  
Iteration 6:   log likelihood = -10954.867  
Iteration 7:   log likelihood = -10954.846  
Iteration 8:   log likelihood = -10954.839  
Iteration 9:   log likelihood = -10954.836  
Iteration 10:  log likelihood = -10954.835  

Two-parameter logistic model                    Number of obs     =      4,591
Log likelihood = -10954.835
------------------------------------------------------------------------------
             |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
officepers   |
     Discrim |   4.074801   .4443495     9.17   0.000     3.203892     4.94571
        Diff |  -.3885263   .0249796   -15.55   0.000    -.4374854   -.3395673
-------------+----------------------------------------------------------------
paramilpers  |
     Discrim |   1.032407   .0584817    17.65   0.000     .9177844    1.147029
        Diff |   .7187132   .0460351    15.61   0.000     .6284861    .8089403
-------------+----------------------------------------------------------------
milnotrial   |
     Discrim |   1.149845   .0643037    17.88   0.000     1.023812    1.275879
        Diff |   .6137702   .0412235    14.89   0.000     .5329736    .6945668
-------------+----------------------------------------------------------------
partye~mpers |
     Discrim |   1.644328   .0915177    17.97   0.000     1.464956    1.823699
        Diff |   .6892289   .0345944    19.92   0.000     .6214251    .7570328
------------------------------------------------------------------------------

.                         predict irtpersB, latent
(option ebmeans assumed)
(using 7 quadrature points)

.                         spearman irtpers8 irtpersA irtpersB 
(obs=4591)

             | irtpers8 irtpersA irtpersB
-------------+---------------------------
    irtpers8 |   1.0000 
    irtpersA |   0.8890   1.0000 
    irtpersB |   0.9452   0.7138   1.0000 

.                         mat a =r(Rho)

.                         local a=a[1,2]

.                         replace corrA=`a' if n==3
(1 real change made)

.                         mat b =r(Rho)

.                         local b=b[1,3]

.                         replace corrB=`b' if n==3
(1 real change made)

.                         drop irtpersA irtpersB

. 
.                         * Cronbach's alpha is a summary of all split-half correlatio
> ns *
.                         alpha sectyapppers officepers paramilpers milmeritpers milno
> trial partyexcompers createparty partyrbr /* 0.7645 */

Test scale = mean(unstandardized items)

Average interitem covariance:     .0627484
Number of items in the scale:            8
Scale reliability coefficient:      0.7645

. 
.                         * we can also think about split-half correlations based on t
> heoretical concepts, which is important in this application because
.                         * it could be that attempts to observe personalist traits va
> ry in their success across leader-time or regime-duration
.                         * or by some structural feature of the country. it may be ea
> sier to observe personalisation early on in a leader or regime life
.                         * because this is most likely when leaders have a strong inc
> entive to personalize and curtail the power of the group that brought
.                         * them to power in the first place (see e.g. Haber's 2010 lo
> gic)
.                         * further it may be more difficult for researchers to observ
> e informal politics in countries that are small or poor
.                         * there is substantially more information in the written rec
> ord on China, for example, than on Gabon; more on Argentina and Brazil than Uruguay 
> and Honduras
.                         * indeed rich countries (Argentina) are more likely to have 
> phd students in social sciences who write dissertations on informal political practi
> ces than poor countries, 
.                         * providing more material with which to observe personalist 
> behavior
. 
.                         centile gwf_case_duration, centile(50)

                                                       -- Binom. Interp. --
    Variable |       Obs  Percentile    Centile        [95% Conf. Interval]
-------------+-------------------------------------------------------------
gwf_case_d~n |     4,591         50          14              14          15

.                         irt 2pl sectyapppers officepers paramilpers milmeritpers mil
> notrial partyexcompers createparty partyrbr if gwf_case_duration<=14

Fitting fixed-effects model:

Iteration 0:   log likelihood =  -11519.86  
Iteration 1:   log likelihood = -11502.633  
Iteration 2:   log likelihood = -11502.623  
Iteration 3:   log likelihood = -11502.623  

Fitting full model:

Iteration 0:   log likelihood = -10508.794  
Iteration 1:   log likelihood = -10195.556  
Iteration 2:   log likelihood = -10175.015  
Iteration 3:   log likelihood = -10174.408  
Iteration 4:   log likelihood = -10174.408  

Two-parameter logistic model                    Number of obs     =      2,329
Log likelihood = -10174.408
------------------------------------------------------------------------------
             |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
sectyapppers |
     Discrim |   1.693693   .1092497    15.50   0.000     1.479568    1.907819
        Diff |   -.158998   .0373531    -4.26   0.000    -.2322088   -.0857872
-------------+----------------------------------------------------------------
officepers   |
     Discrim |   2.614375   .1847961    14.15   0.000     2.252182    2.976569
        Diff |  -.4051468   .0336839   -12.03   0.000    -.4711661   -.3391275
-------------+----------------------------------------------------------------
paramilpers  |
     Discrim |   1.239239   .0879079    14.10   0.000     1.066942    1.411535
        Diff |   .7604202   .0563753    13.49   0.000     .6499266    .8709139
-------------+----------------------------------------------------------------
milmeritpers |
     Discrim |   1.092429    .074916    14.58   0.000     .9455959    1.239261
        Diff |   .3712783   .0503763     7.37   0.000     .2725426     .470014
-------------+----------------------------------------------------------------
milnotrial   |
     Discrim |   1.113775   .0773014    14.41   0.000     .9622674    1.265283
        Diff |   .5383481   .0534364    10.07   0.000     .4336146    .6430815
-------------+----------------------------------------------------------------
partye~mpers |
     Discrim |   2.367619    .200612    11.80   0.000     1.974427    2.760811
        Diff |   .7674018   .0412271    18.61   0.000     .6865982    .8482055
-------------+----------------------------------------------------------------
createparty  |
     Discrim |   .9713244   .0823639    11.79   0.000     .8098942    1.132755
        Diff |   1.869045   .1331357    14.04   0.000     1.608103    2.129986
-------------+----------------------------------------------------------------
partyrbrstmp |
     Discrim |   2.145187   .1751981    12.24   0.000     1.801805    2.488569
        Diff |   .8494162   .0452563    18.77   0.000     .7607156    .9381169
------------------------------------------------------------------------------

.                         predict irtpersA, latent
(option ebmeans assumed)
(using 7 quadrature points)

.                         irt 2pl sectyapppers officepers paramilpers milmeritpers mil
> notrial partyexcompers createparty partyrbr if gwf_case_duration>14

Fitting fixed-effects model:

Iteration 0:   log likelihood = -11357.977  
Iteration 1:   log likelihood = -11339.317  
Iteration 2:   log likelihood = -11339.312  
Iteration 3:   log likelihood = -11339.312  

Fitting full model:

Iteration 0:   log likelihood = -10206.385  
Iteration 1:   log likelihood = -9609.3679  
Iteration 2:   log likelihood = -9565.7611  
Iteration 3:   log likelihood = -9564.5612  
Iteration 4:   log likelihood = -9564.5519  
Iteration 5:   log likelihood =  -9564.552  

Two-parameter logistic model                    Number of obs     =      2,262
Log likelihood =  -9564.552
------------------------------------------------------------------------------
             |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
sectyapppers |
     Discrim |   1.886426   .1167082    16.16   0.000     1.657682     2.11517
        Diff |  -.4999967   .0394927   -12.66   0.000    -.5774011   -.4225924
-------------+----------------------------------------------------------------
officepers   |
     Discrim |   3.113758   .2511753    12.40   0.000     2.621464    3.606052
        Diff |  -.4368032    .032452   -13.46   0.000     -.500408   -.3731983
-------------+----------------------------------------------------------------
paramilpers  |
     Discrim |   1.062741   .0733782    14.48   0.000     .9189227     1.20656
        Diff |   .5602729   .0564314     9.93   0.000     .4496693    .6708765
-------------+----------------------------------------------------------------
milmeritpers |
     Discrim |   1.765999   .1074128    16.44   0.000     1.555474    1.976524
        Diff |   .2591592   .0373853     6.93   0.000     .1858855     .332433
-------------+----------------------------------------------------------------
milnotrial   |
     Discrim |   2.477034   .1669074    14.84   0.000     2.149902    2.804167
        Diff |   .4765397   .0348308    13.68   0.000     .4082726    .5448067
-------------+----------------------------------------------------------------
partye~mpers |
     Discrim |   1.792295   .1198357    14.96   0.000     1.557422    2.027169
        Diff |   .4652174   .0400337    11.62   0.000     .3867529     .543682
-------------+----------------------------------------------------------------
createparty  |
     Discrim |   2.010512   .1473355    13.65   0.000      1.72174    2.299284
        Diff |   1.385853   .0628859    22.04   0.000     1.262599    1.509107
-------------+----------------------------------------------------------------
partyrbrstmp |
     Discrim |    1.77692   .1163863    15.27   0.000     1.548807    2.005033
        Diff |   .5241917   .0410966    12.76   0.000     .4436438    .6047396
------------------------------------------------------------------------------

.                         predict irtpersB, latent
(option ebmeans assumed)
(using 7 quadrature points)

.                         spearman irtpers8 irtpersA irtpersB 
(obs=4591)

             | irtpers8 irtpersA irtpersB
-------------+---------------------------
    irtpers8 |   1.0000 
    irtpersA |   0.9944   1.0000 
    irtpersB |   0.9930   0.9793   1.0000 

.                         mat a =r(Rho)

.                         local a=a[1,2]

.                         replace corrA=`a' if n==4
(1 real change made)

.                         mat b =r(Rho)

.                         local b=b[1,3]

.                         replace corrB=`b' if n==4
(1 real change made)

.                         drop irtpersA irtpersB

. 
.                         centile gwf_leader_duration, centile(50)

                                                       -- Binom. Interp. --
    Variable |       Obs  Percentile    Centile        [95% Conf. Interval]
-------------+-------------------------------------------------------------
gwf_leader~n |     4,591         50           7               7           8

.                         irt 2pl sectyapppers officepers paramilpers milmeritpers mil
> notrial partyexcompers createparty partyrbr if gwf_leader_duration<=7

Fitting fixed-effects model:

Iteration 0:   log likelihood = -10553.861  
Iteration 1:   log likelihood = -10534.838  
Iteration 2:   log likelihood = -10534.761  
Iteration 3:   log likelihood = -10534.761  

Fitting full model:

Iteration 0:   log likelihood = -9742.0068  
Iteration 1:   log likelihood = -9478.4545  
Iteration 2:   log likelihood = -9463.4436  
Iteration 3:   log likelihood = -9462.5309  
Iteration 4:   log likelihood = -9462.5257  
Iteration 5:   log likelihood = -9462.5257  

Two-parameter logistic model                    Number of obs     =      2,356
Log likelihood = -9462.5257
------------------------------------------------------------------------------
             |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
sectyapppers |
     Discrim |   1.660263   .1090241    15.23   0.000      1.44658    1.873947
        Diff |   .1408765   .0370354     3.80   0.000     .0682886    .2134645
-------------+----------------------------------------------------------------
officepers   |
     Discrim |   2.734742   .2119042    12.91   0.000     2.319418    3.150067
        Diff |   .0223464   .0304033     0.73   0.462    -.0372429    .0819357
-------------+----------------------------------------------------------------
paramilpers  |
     Discrim |   1.392571   .1009876    13.79   0.000     1.194639    1.590503
        Diff |    1.00109   .0607308    16.48   0.000     .8820601     1.12012
-------------+----------------------------------------------------------------
milmeritpers |
     Discrim |   1.363758   .0935808    14.57   0.000     1.180343    1.547173
        Diff |   .6021431   .0481772    12.50   0.000     .5077176    .6965687
-------------+----------------------------------------------------------------
milnotrial   |
     Discrim |   1.296595   .0944655    13.73   0.000     1.111446    1.481744
        Diff |   1.013303   .0640764    15.81   0.000      .887716    1.138891
-------------+----------------------------------------------------------------
partye~mpers |
     Discrim |   1.635528   .1346014    12.15   0.000     1.371714    1.899342
        Diff |   1.307864   .0711182    18.39   0.000     1.168474    1.447253
-------------+----------------------------------------------------------------
createparty  |
     Discrim |   .8236342   .0954775     8.63   0.000     .6365018    1.010767
        Diff |   3.011074   .2965014    10.16   0.000     2.429942    3.592206
-------------+----------------------------------------------------------------
partyrbrstmp |
     Discrim |   1.183075   .1029649    11.49   0.000     .9812677    1.384883
        Diff |   1.561997   .1043015    14.98   0.000      1.35757    1.766425
------------------------------------------------------------------------------

.                         predict irtpersA, latent
(option ebmeans assumed)
(using 7 quadrature points)

.                         irt 2pl sectyapppers officepers paramilpers milmeritpers mil
> notrial partyexcompers createparty partyrbr if gwf_leader_duration>7

Fitting fixed-effects model:

Iteration 0:   log likelihood =  -11323.71  
Iteration 1:   log likelihood = -11315.269  
Iteration 2:   log likelihood = -11315.263  
Iteration 3:   log likelihood = -11315.263  

Fitting full model:

Iteration 0:   log likelihood = -10499.416  
Iteration 1:   log likelihood = -10005.491  
Iteration 2:   log likelihood = -9990.4761  
Iteration 3:   log likelihood = -9990.0359  
Iteration 4:   log likelihood = -9990.0349  
Iteration 5:   log likelihood = -9990.0349  

Two-parameter logistic model                    Number of obs     =      2,235
Log likelihood = -9990.0349
------------------------------------------------------------------------------
             |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
sectyapppers |
     Discrim |   1.530284   .1109629    13.79   0.000       1.3128    1.747767
        Diff |  -.9417338   .0571874   -16.47   0.000    -1.053819   -.8296485
-------------+----------------------------------------------------------------
officepers   |
     Discrim |   2.335118   .1835251    12.72   0.000     1.975415     2.69482
        Diff |  -1.036678   .0488424   -21.22   0.000    -1.132407   -.9409483
-------------+----------------------------------------------------------------
paramilpers  |
     Discrim |   .7728726   .0689406    11.21   0.000     .6377514    .9079937
        Diff |    .300633   .0658036     4.57   0.000     .1716602    .4296058
-------------+----------------------------------------------------------------
milmeritpers |
     Discrim |   1.378612   .0969665    14.22   0.000     1.188561    1.568663
        Diff |   .0135487   .0416992     0.32   0.745    -.0681803    .0952777
-------------+----------------------------------------------------------------
milnotrial   |
     Discrim |   1.664458   .1194293    13.94   0.000     1.430381    1.898535
        Diff |    .116961   .0380727     3.07   0.002     .0423398    .1915821
-------------+----------------------------------------------------------------
partye~mpers |
     Discrim |   1.902996   .1640496    11.60   0.000     1.581464    2.224527
        Diff |    .135001   .0361011     3.74   0.000     .0642442    .2057579
-------------+----------------------------------------------------------------
createparty  |
     Discrim |   1.328945   .1013098    13.12   0.000     1.130382    1.527509
        Diff |   1.198929   .0738961    16.22   0.000     1.054096    1.343763
-------------+----------------------------------------------------------------
partyrbrstmp |
     Discrim |   2.275165   .2078068    10.95   0.000     1.867871    2.682459
        Diff |   .2282116   .0344883     6.62   0.000     .1606158    .2958075
------------------------------------------------------------------------------

.                         predict irtpersB, latent
(option ebmeans assumed)
(using 7 quadrature points)

.                         spearman irtpers8 irtpersA irtpersB 
(obs=4591)

             | irtpers8 irtpersA irtpersB
-------------+---------------------------
    irtpers8 |   1.0000 
    irtpersA |   0.9910   1.0000 
    irtpersB |   0.9983   0.9850   1.0000 

.                         mat a =r(Rho)

.                         local a=a[1,2]

.                         replace corrA=`a' if n==5
(1 real change made)

.                         mat b =r(Rho)

.                         local b=b[1,3]

.                         replace corrB=`b' if n==5
(1 real change made)

.                         drop irtpersA irtpersB

. 
.                         centile gdpcap, centile(50)

                                                       -- Binom. Interp. --
    Variable |       Obs  Percentile    Centile        [95% Conf. Interval]
-------------+-------------------------------------------------------------
      gdpcap |     4,511         50    1.802731        1.727016    1.911888

.                         irt 2pl sectyapppers officepers paramilpers milmeritpers mil
> notrial partyexcompers createparty partyrbr if gdpcap<=1.80

Fitting fixed-effects model:

Iteration 0:   log likelihood =  -11453.79  
Iteration 1:   log likelihood = -11436.934  
Iteration 2:   log likelihood = -11436.925  
Iteration 3:   log likelihood = -11436.925  

Fitting full model:

Iteration 0:   log likelihood = -10425.632  
Iteration 1:   log likelihood = -9980.5481  
Iteration 2:   log likelihood = -9949.7766  
Iteration 3:   log likelihood = -9947.4057  
Iteration 4:   log likelihood = -9947.3941  
Iteration 5:   log likelihood = -9947.3942  

Two-parameter logistic model                    Number of obs     =      2,251
Log likelihood = -9947.3942
------------------------------------------------------------------------------
             |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
sectyapppers |
     Discrim |    1.58171   .1023265    15.46   0.000     1.381154    1.782267
        Diff |  -.5083713   .0436191   -11.65   0.000    -.5938631   -.4228795
-------------+----------------------------------------------------------------
officepers   |
     Discrim |   2.224957   .1493153    14.90   0.000     1.932304     2.51761
        Diff |  -.6332835   .0393038   -16.11   0.000    -.7103176   -.5562494
-------------+----------------------------------------------------------------
paramilpers  |
     Discrim |   1.108885   .0772601    14.35   0.000      .957458    1.260312
        Diff |   .4370042   .0521072     8.39   0.000     .3348761    .5391324
-------------+----------------------------------------------------------------
milmeritpers |
     Discrim |    1.12843   .0770493    14.65   0.000      .977416    1.279444
        Diff |   .2006754   .0478502     4.19   0.000     .1068907      .29446
-------------+----------------------------------------------------------------
milnotrial   |
     Discrim |   1.312028   .0882273    14.87   0.000     1.139106     1.48495
        Diff |   .1079522   .0429598     2.51   0.012     .0237525    .1921519
-------------+----------------------------------------------------------------
partye~mpers |
     Discrim |   2.696911   .2145856    12.57   0.000     2.276331    3.117491
        Diff |   .5410399   .0351096    15.41   0.000     .4722264    .6098534
-------------+----------------------------------------------------------------
createparty  |
     Discrim |   1.342901     .09549    14.06   0.000     1.155744    1.530058
        Diff |   1.335349   .0761706    17.53   0.000     1.186057     1.48464
-------------+----------------------------------------------------------------
partyrbrstmp |
     Discrim |   3.004125   .2602348    11.54   0.000     2.494075    3.514176
        Diff |   .5856028   .0347613    16.85   0.000     .5174718    .6537337
------------------------------------------------------------------------------

.                         predict irtpersA, latent
(option ebmeans assumed)
(using 7 quadrature points)

.                         irt 2pl sectyapppers officepers paramilpers milmeritpers mil
> notrial partyexcompers createparty partyrbr if gdpcap>1.80

Fitting fixed-effects model:

Iteration 0:   log likelihood = -11298.377  
Iteration 1:   log likelihood = -11276.827  
Iteration 2:   log likelihood = -11276.809  
Iteration 3:   log likelihood = -11276.809  

Fitting full model:

Iteration 0:   log likelihood =  -10229.75  
Iteration 1:   log likelihood = -9783.7284  
Iteration 2:   log likelihood = -9745.5613  
Iteration 3:   log likelihood = -9738.9087  
Iteration 4:   log likelihood = -9737.7184  
Iteration 5:   log likelihood = -9737.4386  
Iteration 6:   log likelihood = -9737.3779  
Iteration 7:   log likelihood = -9737.3157  
Iteration 8:   log likelihood = -9737.2091  
Iteration 9:   log likelihood = -9736.8636  
Iteration 10:  log likelihood = -9735.6736  
Iteration 11:  log likelihood = -9734.3453  
Iteration 12:  log likelihood = -9733.6048  
Iteration 13:  log likelihood = -9733.9202  
Iteration 14:  log likelihood = -9734.2014  
Iteration 15:  log likelihood = -9734.3312  
Iteration 16:  log likelihood = -9734.3922  
Iteration 17:  log likelihood = -9734.4186  
Iteration 18:  log likelihood = -9734.4295  
Iteration 19:  log likelihood = -9734.4339  
Iteration 20:  log likelihood = -9734.4356  
Iteration 21:  log likelihood = -9734.4363  

Two-parameter logistic model                    Number of obs     =      2,340
Log likelihood = -9734.4363
------------------------------------------------------------------------------
             |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
sectyapppers |
     Discrim |   1.832356   .1065363    17.20   0.000     1.623549    2.041163
        Diff |  -.1255871   .0351046    -3.58   0.000    -.1943908   -.0567833
-------------+----------------------------------------------------------------
officepers   |
     Discrim |   14.08474   1.899153     7.42   0.000     10.36247    17.80701
        Diff |  -.1499193   .0202964    -7.39   0.000    -.1896995    -.110139
-------------+----------------------------------------------------------------
paramilpers  |
     Discrim |   1.004072   .0716159    14.02   0.000      .863708    1.144437
        Diff |   1.044277   .0730032    14.30   0.000     .9011933    1.187361
-------------+----------------------------------------------------------------
milmeritpers |
     Discrim |   1.525035   .0912551    16.71   0.000     1.346178    1.703892
        Diff |   .4655018   .0405469    11.48   0.000     .3860313    .5449722
-------------+----------------------------------------------------------------
milnotrial   |
     Discrim |   1.640665   .1004256    16.34   0.000     1.443834    1.837495
        Diff |   .9720309   .0502376    19.35   0.000      .873567    1.070495
-------------+----------------------------------------------------------------
partye~mpers |
     Discrim |   1.847558   .1178503    15.68   0.000     1.616575     2.07854
        Diff |   .7207653   .0417439    17.27   0.000     .6389488    .8025818
-------------+----------------------------------------------------------------
createparty  |
     Discrim |   1.140183   .0954157    11.95   0.000     .9531716    1.327194
        Diff |   2.167477   .1384596    15.65   0.000     1.896101    2.438852
-------------+----------------------------------------------------------------
partyrbrstmp |
     Discrim |   1.524464   .0980784    15.54   0.000     1.332233    1.716694
        Diff |   .8363755   .0494019    16.93   0.000     .7395496    .9332013
------------------------------------------------------------------------------

.                         predict irtpersB, latent
(option ebmeans assumed)
(using 7 quadrature points)

.                         spearman irtpers8 irtpersA irtpersB 
(obs=4591)

             | irtpers8 irtpersA irtpersB
-------------+---------------------------
    irtpers8 |   1.0000 
    irtpersA |   0.9895   1.0000 
    irtpersB |   0.9863   0.9638   1.0000 

.                         mat a =r(Rho)

.                         local a=a[1,2]

.                         replace corrA=`a' if n==6
(1 real change made)

.                         mat b =r(Rho)

.                         local b=b[1,3]

.                         replace corrB=`b' if n==6
(1 real change made)

.                         drop irtpersA irtpersB

. 
.                         centile popav, centile(50)

                                                       -- Binom. Interp. --
    Variable |       Obs  Percentile    Centile        [95% Conf. Interval]
-------------+-------------------------------------------------------------
      popavg |     4,515         50    9156.481        8866.008    9648.493

.                         irt 2pl sectyapppers officepers paramilpers milmeritpers mil
> notrial partyexcompers createparty partyrbr if popav<=9156.481

Fitting fixed-effects model:

Iteration 0:   log likelihood = -11201.747  
Iteration 1:   log likelihood = -11184.784  
Iteration 2:   log likelihood = -11184.776  
Iteration 3:   log likelihood = -11184.776  

Fitting full model:

Iteration 0:   log likelihood = -10354.954  
Iteration 1:   log likelihood = -9893.4008  
Iteration 2:   log likelihood = -9856.3358  
Iteration 3:   log likelihood = -9850.6039  
Iteration 4:   log likelihood = -9850.4008  
Iteration 5:   log likelihood = -9850.4005  

Two-parameter logistic model                    Number of obs     =      2,257
Log likelihood = -9850.4005
------------------------------------------------------------------------------
             |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
sectyapppers |
     Discrim |   1.323859    .103967    12.73   0.000     1.120088    1.527631
        Diff |  -.4055088   .0471267    -8.60   0.000    -.4978754   -.3131421
-------------+----------------------------------------------------------------
officepers   |
     Discrim |     3.4508   .3656651     9.44   0.000     2.734109     4.16749
        Diff |  -.7248406   .0372761   -19.45   0.000    -.7979005   -.6517807
-------------+----------------------------------------------------------------
paramilpers  |
     Discrim |   1.015697   .0850053    11.95   0.000     .8490901    1.182305
        Diff |   .6627393   .0651788    10.17   0.000     .5349912    .7904873
-------------+----------------------------------------------------------------
milmeritpers |
     Discrim |   .8899442   .0693496    12.83   0.000     .7540215    1.025867
        Diff |   .0427271   .0554181     0.77   0.441    -.0658903    .1513445
-------------+----------------------------------------------------------------
milnotrial   |
     Discrim |   1.063105    .077019    13.80   0.000     .9121506     1.21406
        Diff |   .5635878   .0575963     9.79   0.000     .4507012    .6764743
-------------+----------------------------------------------------------------
partye~mpers |
     Discrim |   3.601619   .5278296     6.82   0.000     2.567092    4.636146
        Diff |   .4903907   .0340729    14.39   0.000     .4236091    .5571723
-------------+----------------------------------------------------------------
createparty  |
     Discrim |   1.040368   .0902452    11.53   0.000     .8634902    1.217245
        Diff |   1.895036   .1337526    14.17   0.000     1.632886    2.157187
-------------+----------------------------------------------------------------
partyrbrstmp |
     Discrim |   2.623121    .262281    10.00   0.000      2.10906    3.137183
        Diff |   .5433025   .0375347    14.47   0.000     .4697358    .6168692
------------------------------------------------------------------------------

.                         predict irtpersA, latent
(option ebmeans assumed)
(using 7 quadrature points)

.                         irt 2pl sectyapppers officepers paramilpers milmeritpers mil
> notrial partyexcompers createparty partyrbr if popav>9156.481

Fitting fixed-effects model:

Iteration 0:   log likelihood = -11616.524  
Iteration 1:   log likelihood = -11597.419  
Iteration 2:   log likelihood =  -11597.41  
Iteration 3:   log likelihood =  -11597.41  

Fitting full model:

Iteration 0:   log likelihood = -10301.453  
Iteration 1:   log likelihood = -9875.8183  
Iteration 2:   log likelihood = -9849.1588  
Iteration 3:   log likelihood = -9848.7864  
Iteration 4:   log likelihood = -9848.7865  

Two-parameter logistic model                    Number of obs     =      2,334
Log likelihood = -9848.7865
------------------------------------------------------------------------------
             |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
sectyapppers |
     Discrim |   2.062143   .1216849    16.95   0.000     1.823645    2.300641
        Diff |  -.2913799   .0351785    -8.28   0.000    -.3603285   -.2224313
-------------+----------------------------------------------------------------
officepers   |
     Discrim |   2.811769   .1858801    15.13   0.000      2.44745    3.176087
        Diff |  -.1180295   .0305326    -3.87   0.000    -.1778724   -.0581866
-------------+----------------------------------------------------------------
paramilpers  |
     Discrim |   1.034381    .071402    14.49   0.000     .8944356    1.174326
        Diff |   .7673145   .0623434    12.31   0.000     .6451237    .8895052
-------------+----------------------------------------------------------------
milmeritpers |
     Discrim |   1.831491   .1148004    15.95   0.000     1.606487    2.056496
        Diff |   .5118013    .038758    13.21   0.000      .435837    .5877655
-------------+----------------------------------------------------------------
milnotrial   |
     Discrim |    2.04634   .1299737    15.74   0.000     1.791596    2.301084
        Diff |   .5094461   .0368057    13.84   0.000     .4373082     .581584
-------------+----------------------------------------------------------------
partye~mpers |
     Discrim |   1.901464   .1240174    15.33   0.000     1.658394    2.144533
        Diff |   .6719835    .041198    16.31   0.000     .5912368    .7527301
-------------+----------------------------------------------------------------
createparty  |
     Discrim |   1.379285   .1010825    13.65   0.000     1.181167    1.577403
        Diff |    1.57078   .0862132    18.22   0.000     1.401805    1.739755
-------------+----------------------------------------------------------------
partyrbrstmp |
     Discrim |   2.000442   .1332407    15.01   0.000     1.739295    2.261589
        Diff |   .7566939   .0420599    17.99   0.000      .674258    .8391299
------------------------------------------------------------------------------

.                         predict irtpersB, latent
(option ebmeans assumed)
(using 7 quadrature points)

.                         spearman irtpers8 irtpersA irtpersB 
(obs=4591)

             | irtpers8 irtpersA irtpersB
-------------+---------------------------
    irtpers8 |   1.0000 
    irtpersA |   0.9832   1.0000 
    irtpersB |   0.9966   0.9691   1.0000 

.                         mat a =r(Rho)

.                         local a=a[1,2]

.                         replace corrA=`a' if n==7
(1 real change made)

.                         mat b =r(Rho)

.                         local b=b[1,3]

.                         replace corrB=`b' if n==7
(1 real change made)

.                         drop irtpersA irtpersB

. 
.                         graph bar corrA corrB if n<8,over(n ,relabel(1 "Year" 2 "Ran
> dom" 3 "Item" 4 `""Leader" "time""' 5 `""Regime" "duration""' 6 "GDPpc" 7 "Populatio
> n")) ///
>                          exclude0 ysc(range(0.8,1)) ylab(0.8(.05)1) ytit("Spearman c
> orrelation with IRT-8") bargap(.5) blab(bar, size(vsmall) col(gs1) format(%9.3f) pos
> ition(inside)) ///
>                          legend(lab(1 "Group 1") lab(2 "Group 2") pos(6) col(2))

.                         graph export "$dir/golden/Reliability-tests.pdf",as(pdf) rep
> lace
(file /Users/lee/Dropbox/Datavers/golden/Reliability-tests.pdf written in PDF format)

. 
. 
. ****************
. *** Validity ***
. ****************
. 
.                 * Face validity is the China examples and Libya example; See also So
> ng and Wright for North Korea test 
.                 * Also show face validity in Appendix with *
.                                         label var xirtpers8 "Personalism index"

.                                   ** Zaire over time **
.                                   twoway (line xirtpers8 year if cow==490 & year>196
> 4 & year<1998,ysc(range(0,1)) xscale(range (1965 1995)) ylab(0(.2)1,glcol(gs15)) xla
> bel(1965 (5) 1995) /*
>                                   */ title("Mobutu's regime in the former Zaire") yt
> itle("Personalist index") xtitle("Year")  legend(pos(12) col(1) ring(1)) saving(h4.g
> ph,replace))   
(file h4.gph saved)

.                                   ** Guinea over time **
.                                   twoway (line xirtpers8 year if cow==438 & year>198
> 4 & year<2009,ysc(range(0,1)) xscale(range (1985 2005)) ylab(0(.2)1,glcol(gs15)) xla
> bel(1985 (5) 2005) /*
>                                   */ title("Conte's regime in Guinea") ytitle("Perso
> nalist index") xtitle("Year")  legend(pos(12) col(1) ring(1)) saving(h2.gph,replace)
> )   
(file h2.gph saved)

.                                   ** Albania over time **
.                                   twoway (line xirtpers8 year if cow==339 & year>194
> 5 & year<1992,ysc(range(0,1)) xscale(range (1950 1990)) ylab(0(.2)1,glcol(gs15)) xla
> bel(1950 (10) 1990) /*
>                                   */ title("Albania") ytitle("Personalist index") xt
> itle("Year")  legend(pos(12) col(1) ring(1)) saving(h1.gph,replace))  
(file h1.gph saved)

.                                         ** North Korea over time **
.                                   twoway (line xirtpers8 year if cow==731 & year>194
> 7 & year<2011,ysc(range(0,1)) xscale(range (1950 2010)) ylab(0(.2)1,glcol(gs15)) xla
> bel(1950 (10) 2010) /*
>                                   */ title("Kim regime in North Korea") ytitle("Pers
> onalist index") xtitle("Year")  legend(pos(12) col(1) ring(1)) saving(h3.gph,replace
> )) 
(file h3.gph saved)

.                                   gr combine h1.gph h2.gph h3.gph h4.gph,col(2)

.                                   graph export "$dir/golden/Regime-Examples.pdf", as
> (pdf)  replace
(file /Users/lee/Dropbox/Datavers/golden/Regime-Examples.pdf written in PDF format)

.                                   erase h1.gph

.                                   erase h2.gph

.                                   erase h3.gph

.                                   erase h4.gph

. 
.                 * Content validity can be shown is the discussion in the Appendix Fi
> gure C-1 *
. 
.                 * Convergent (concurrent) validity is established in Figure C-5, whi
> ch shows that the Personalism index is correlated with GWF and Weeks measures *
. 
.                 * Discriminant validity is established by showing low within-unit co
> rrelation with Polity and Vdem measures (Table B-2, Figures B-3 to B-19); low correl
> ation with extant
.                 * military and party variables with IRTPers-8 
. 
.                                 * Correlations with extant party and military variab
> les *
.                                 use temp,clear

.                                 sort cow  year

.                                 merge cow year using ATH
(note: you are using old merge syntax; see [D] merge for new syntax)
(note: variable country was str32, now str33 to accommodate using data's values)

.                                 tab _merge

     _merge |      Freq.     Percent        Cum.
------------+-----------------------------------
          2 |      1,084       19.10       19.10
          3 |      4,591       80.90      100.00
------------+-----------------------------------
      Total |      5,675      100.00

.                                 drop if gwf_caseid==.
(1,084 observations deleted)

.                                 gen cg_mil = cg_regime==4 if cg_regime~=.
(331 missing values generated)

.                                 gen cg_party =  cg_gparties>=1 if cg_gparties~=.
(205 missing values generated)

.                                 gen dpi_party = dpi_parties >=1 if dpi_parties~=.
(2,053 missing values generated)

.                                 local var = "cg_gparties ht_parties dpi_parties sv_p
> arties cg_party ht_party dpi_party sv_party lparty cg_ginst sv_legindex dpi_liec cg_
> mil ht_mil dpi_mil sv_military sv_mil_corp sv_mil_pers sv_mil_ind"

.                                 foreach v of local var {
  2.                                         qui recode `v' (-66=.) (-77=.) (-88=.) (-
> 99=.)
  3.                                 }

.                                 * Construct correlation matrix
.                                 matrix m = J(19,1,.)

.                                 local dimensions = "xirtpers"

.                                 local i=1

.                                 xtset gwf_caseid year
       panel variable:  gwf_caseid (unbalanced)
        time variable:  year, 1946 to 2010
                delta:  1 unit

.                                 local vars = "cg_gparties ht_parties dpi_parties sv_
> parties cg_party ht_party dpi_party sv_party lparty cg_ginst sv_legindex dpi_liec cg
> _mil ht_mil dpi_mil sv_military sv_mil_corp sv_mil_pers sv_mil_ind xirtpers8"

.                                 foreach t of local vars {
  2.                                         qui egen m`t'=mean(`t'),by(gwf_caseid)
  3.                                         qui gen r`t'=`t'-m`t'
  4.                                 }

.                                 foreach t of local dimensions {
  2.                                         local j = 1
  3.                                         local klass = "cg_gparties ht_parties dpi
> _parties sv_parties cg_party ht_party dpi_party sv_party lparty cg_ginst sv_legindex
>  dpi_liec cg_mil ht_mil dpi_mil sv_military sv_mil_corp sv_mil_pers sv_mil_ind"
  4.                                         foreach k of local klass {
  5.                                                 qui spearman r`t' r`k'
  6.                                                 matrix j = r(rho)
  7.                                                 local s = round(j[1,1],.001)
  8.                                                 local f = `s'
  9.                                                 matrix m[`j',`i'] =`f'
 10.                                                 local j= `j' + 1
 11.                                         }
 12.                                         local i = `i'+1
 13.                                 }

.                                 mat rown m= `klass'

.                                 estout matrix(m),style(tex)

            &           m\\
            &          c1\\
cg_gparties &           0\\
ht_parties  &       -.054\\
dpi_parties &        .132\\
sv_parties  &        .002\\
cg_party    &        .112\\
ht_party    &        .184\\
dpi_party   &        .176\\
sv_party    &        .117\\
lparty      &        .081\\
cg_ginst    &        .077\\
sv_legindex &        .082\\
dpi_liec    &        .139\\
cg_mil      &        .136\\
ht_mil      &        -.13\\
dpi_mil     &        .095\\
sv_military &        .153\\
sv_mil_corp &       -.039\\
sv_mil_pers &        .185\\
sv_mil_ind  &       -.023\\

. 
.                         * correlations among personalist measures *
.                                 use temp,clear

.                                 xtset gwf_caseid year
       panel variable:  gwf_caseid (unbalanced)
        time variable:  year, 1946 to 2010
                delta:  1 unit

.                                 local vars = "Personalist persrat_1a sv_mil_pers sv_
> mil_corp irtpers8 xconst gwf_leader_duration"

.                                 foreach t of local vars {
  2.                                         qui egen m`t'=mean(`t'),by(gwf_caseid)
  3.                                         qui gen r`t'=`t'-m`t'
  4.                                 }

.                                 matrix m = J(6,3,.)

.                                 local klass = "Personalist persrat_1a sv_mil_pers sv
> _mil_corp xconst gwf_leader_duration"

.                                 local j =1

.                                 foreach k of local klass {
  2.                                         qui spearman  irtpers8  `k' /* within cor
> relation */
  3.                                         matrix j = r(rho)
  4.                                         local s = round(j[1,1],.001)
  5.                                         local f = `s'
  6.                                         matrix m[`j',1] =`f'
  7.                                         qui spearman rirtpers8 r`k' /* within cor
> relation */
  8.                                         matrix j = r(rho)
  9.                                         local s = round(j[1,1],.001)
 10.                                         local f = `s'
 11.                                         matrix m[`j',2] =`f'
 12.                                         qui spearman mirtpers8 m`k'  /* between c
> orrelation */
 13.                                         matrix j = r(rho)
 14.                                         local s = round(j[1,1],.001)
 15.                                         local f = `s'
 16.                                         matrix m[`j',3] =`f'
 17.                                         local j= `j' + 1
 18.                                 }

.                                 mat rown m= `klass'

.                                 mat coln m= overall within between

.                                 estout matrix(m),style(tex)

            &           m&            &            \\
            &     overall&      within&     between\\
Personalist &        .388&           .&        .462\\
persrat_1a  &        .569&        .213&        .647\\
sv_mil_pers &        .198&        .185&         .17\\
sv_mil_corp &         .04&       -.039&        .053\\
xconst      &       -.301&       -.032&       -.244\\
gwf_leader_duration&        .446&        .409&        .467\\

.                  
.                                 ****************************************************
> **
.                                 ********** Democracy correlation matrix ************
> **
.                                 ****************************************************
> **
.                                 use temp,clear

.                                 gen Duration = ln(gwf_leader_duration)
(11 missing values generated)

.                                 local var = "polity2"

.                                 foreach v of local var {
  2.                                         recode `v' (-66=.) (-77=.) (-88=.) (-99=.
> )
  3.                                 }
(polity2: 0 changes made)

.                                 * Construct correlation matrix
.                                 matrix m = J(5,8,.)

.                                 matrix colnames m = Party Military Personal uds_mean
>  polity2 vdem FH Duration

.                                 matrix list m

m[5,8]
       Party  Military  Personal  uds_mean   polity2      vdem        FH  Duration
r1         .         .         .         .         .         .         .         .
r2         .         .         .         .         .         .         .         .
r3         .         .         .         .         .         .         .         .
r4         .         .         .         .         .         .         .         .
r5         .         .         .         .         .         .         .         .

.                                 local dimensions = "uds_mean polity2 v2x_polyarchy f
> h_scale Duration"

.                                 local j=1

.                                 foreach t of local dimensions {
  2.                                         local i = 1
  3.                                         local klass = "pr1 pr2 pr3 uds_mean polit
> y2 v2x_polyarchy fh_scale Duration"
  4.                                         foreach k of local klass {
  5.                                                 spearman `t' `k'
  6.                                                 local rho = r(rho)
  7.                                                 local s = round(`rho',.01)
  8.                                                 local f = abs(`s')
  9.                                                 matrix m[`j',`i'] =`f'
 10.                                                 local i= `i' + 1
 11.                                         }
 12.                                         local j = `j'+1
 13.                                 }

 Number of obs =    4552
Spearman's rho =       0.1444

Test of Ho: uds_mean and pr1 are independent
    Prob > |t| =       0.0000

 Number of obs =    4552
Spearman's rho =      -0.1617

Test of Ho: uds_mean and pr2 are independent
    Prob > |t| =       0.0000

 Number of obs =    4552
Spearman's rho =      -0.1828

Test of Ho: uds_mean and pr3 are independent
    Prob > |t| =       0.0000

 Number of obs =    4552
Spearman's rho =       1.0000

Test of Ho: uds_mean and uds_mean are independent
    Prob > |t| =       0.0000

 Number of obs =    4516
Spearman's rho =       0.8603

Test of Ho: uds_mean and polity2 are independent
    Prob > |t| =       0.0000

 Number of obs =    4454
Spearman's rho =       0.7224

Test of Ho: uds_mean and v2x_polyarchy are independent
    Prob > |t| =       0.0000

 Number of obs =    2926
Spearman's rho =      -0.8499

Test of Ho: uds_mean and fh_scale are independent
    Prob > |t| =       0.0000

 Number of obs =    4552
Spearman's rho =      -0.1671

Test of Ho: uds_mean and Duration are independent
    Prob > |t| =       0.0000

 Number of obs =    4553
Spearman's rho =       0.0756

Test of Ho: polity2 and pr1 are independent
    Prob > |t| =       0.0000

 Number of obs =    4553
Spearman's rho =      -0.0279

Test of Ho: polity2 and pr2 are independent
    Prob > |t| =       0.0598

 Number of obs =    4553
Spearman's rho =      -0.2495

Test of Ho: polity2 and pr3 are independent
    Prob > |t| =       0.0000

 Number of obs =    4516
Spearman's rho =       0.8603

Test of Ho: polity2 and uds_mean are independent
    Prob > |t| =       0.0000

 Number of obs =    4553
Spearman's rho =       1.0000

Test of Ho: polity2 and polity2 are independent
    Prob > |t| =       0.0000

 Number of obs =    4450
Spearman's rho =       0.5801

Test of Ho: polity2 and v2x_polyarchy are independent
    Prob > |t| =       0.0000

 Number of obs =    2904
Spearman's rho =      -0.5936

Test of Ho: polity2 and fh_scale are independent
    Prob > |t| =       0.0000

 Number of obs =    4542
Spearman's rho =      -0.2415

Test of Ho: polity2 and Duration are independent
    Prob > |t| =       0.0000

 Number of obs =    4487
Spearman's rho =       0.2969

Test of Ho: v2x_polyarchy and pr1 are independent
    Prob > |t| =       0.0000

 Number of obs =    4487
Spearman's rho =      -0.2883

Test of Ho: v2x_polyarchy and pr2 are independent
    Prob > |t| =       0.0000

 Number of obs =    4487
Spearman's rho =      -0.1389

Test of Ho: v2x_polyarchy and pr3 are independent
    Prob > |t| =       0.0000

 Number of obs =    4454
Spearman's rho =       0.7224

Test of Ho: v2x_polyarchy and uds_mean are independent
    Prob > |t| =       0.0000

 Number of obs =    4450
Spearman's rho =       0.5801

Test of Ho: v2x_polyarchy and polity2 are independent
    Prob > |t| =       0.0000

 Number of obs =    4487
Spearman's rho =       1.0000

Test of Ho: v2x_polyarchy and v2x_polyarchy are independent
    Prob > |t| =       0.0000

 Number of obs =    2896
Spearman's rho =      -0.6477

Test of Ho: v2x_polyarchy and fh_scale are independent
    Prob > |t| =       0.0000

 Number of obs =    4476
Spearman's rho =      -0.0434

Test of Ho: v2x_polyarchy and Duration are independent
    Prob > |t| =       0.0037

 Number of obs =    2927
Spearman's rho =      -0.0434

Test of Ho: fh_scale and pr1 are independent
    Prob > |t| =       0.0189

 Number of obs =    2927
Spearman's rho =       0.2498

Test of Ho: fh_scale and pr2 are independent
    Prob > |t| =       0.0000

 Number of obs =    2927
Spearman's rho =       0.1515

Test of Ho: fh_scale and pr3 are independent
    Prob > |t| =       0.0000

 Number of obs =    2926
Spearman's rho =      -0.8499

Test of Ho: fh_scale and uds_mean are independent
    Prob > |t| =       0.0000

 Number of obs =    2904
Spearman's rho =      -0.5936

Test of Ho: fh_scale and polity2 are independent
    Prob > |t| =       0.0000

 Number of obs =    2896
Spearman's rho =      -0.6477

Test of Ho: fh_scale and v2x_polyarchy are independent
    Prob > |t| =       0.0000

 Number of obs =    2927
Spearman's rho =       1.0000

Test of Ho: fh_scale and fh_scale are independent
    Prob > |t| =       0.0000

 Number of obs =    2927
Spearman's rho =       0.1075

Test of Ho: fh_scale and Duration are independent
    Prob > |t| =       0.0000

 Number of obs =    4580
Spearman's rho =       0.2152

Test of Ho: Duration and pr1 are independent
    Prob > |t| =       0.0000

 Number of obs =    4580
Spearman's rho =      -0.1436

Test of Ho: Duration and pr2 are independent
    Prob > |t| =       0.0000

 Number of obs =    4580
Spearman's rho =       0.3513

Test of Ho: Duration and pr3 are independent
    Prob > |t| =       0.0000

 Number of obs =    4552
Spearman's rho =      -0.1671

Test of Ho: Duration and uds_mean are independent
    Prob > |t| =       0.0000

 Number of obs =    4542
Spearman's rho =      -0.2415

Test of Ho: Duration and polity2 are independent
    Prob > |t| =       0.0000

 Number of obs =    4476
Spearman's rho =      -0.0434

Test of Ho: Duration and v2x_polyarchy are independent
    Prob > |t| =       0.0037

 Number of obs =    2927
Spearman's rho =       0.1075

Test of Ho: Duration and fh_scale are independent
    Prob > |t| =       0.0000

 Number of obs =    4580
Spearman's rho =       1.0000

Test of Ho: Duration and Duration are independent
    Prob > |t| =            .

.                                 matrix list m

m[5,8]
       Party  Military  Personal  uds_mean   polity2      vdem        FH  Duration
r1       .14       .16       .18         1       .86       .72       .85       .17
r2       .08       .03       .25       .86         1       .58       .59       .24
r3        .3       .29       .14       .72       .58         1       .65       .04
r4       .04       .25       .15       .85       .59       .65         1       .11
r5       .22       .14       .35       .17       .24       .04       .11         1

.                                 plotmatrix, m(m) c(yellow) legend(off)   freq  split
> (0(.01)1)  xsize(3) ysize(2) /*
>                                 */ xlab(1 "{bf:Party}" 2 "{bf:Military}" 3 "{bf:Pers
> onal}" 4 "UDS" 5 "Polity2" 6 "VDem" 7 "Freedom House" 8 "Leader duration" , angle(45
> )) /*
>                                 */ ylab(0  "UDS" -1 "Polity2" -2 "VDem" -3 "Freedom 
> House" -4 "Leader duration") 
WARNING: Tested only for Stata version 15.1 and higher.
Your Stata version 14.2 is not officially supported.
SPLIT 0 .01 .02 .03 .04 .05 .06 .07 .08 .09 .1 .11 .12 .13 .14 .15 .16 .17 .18 .19 .2 
> .21 .22 .23 .24 .25 .26 .27 .28 .29 .3 .31 .32 .33 .34 .35 .36 .37 .38 .39 .4 .41 .4
> 2 .43 .44 .45 .46 .47 .48 .49 .5 .51 .52 .53 .54 .55 .56 .57 .58 .59 .6 .61 .62 .63 
> .64 .65 .66 .67 .68 .69 .7 .71 .72 .73 .74 .75 .76 .77 .78 .79 .8 .81 .82 .83 .84 .8
> 5 .86 .87 .88 .89 .9 .91 .92 .93 .94 .95 .96 .97 .98 .99 1

.                                 graph export "$dir/golden/CorrDem.pdf",as(pdf) repla
> ce
(file /Users/lee/Dropbox/Datavers/golden/CorrDem.pdf written in PDF format)

. 
.                                 
.                                 use temp,clear

.                                 local var = "xrreg xrcomp xropen xconst parreg parco
> mp  polcomp"

.                                 foreach v of local var {
  2.                                         recode `v' (-66=.) (-77=.) (-88=.) (-99=.
> )
  3.                                 }               
(xrreg: 174 changes made)
(xrcomp: 174 changes made)
(xropen: 174 changes made)
(xconst: 174 changes made)
(parreg: 174 changes made)
(parcomp: 174 changes made)
(polcomp: 174 changes made)

.                                 matrix m = J(5,16,.)

.                                 matrix list m

m[5,16]
     c1   c2   c3   c4   c5   c6   c7   c8   c9  c10  c11  c12  c13  c14  c15  c16
r1    .    .    .    .    .    .    .    .    .    .    .    .    .    .    .    .
r2    .    .    .    .    .    .    .    .    .    .    .    .    .    .    .    .
r3    .    .    .    .    .    .    .    .    .    .    .    .    .    .    .    .
r4    .    .    .    .    .    .    .    .    .    .    .    .    .    .    .    .
r5    .    .    .    .    .    .    .    .    .    .    .    .    .    .    .    .

.                                 local dimensions = "pr1 pr2 pr3 polity2 v2x_polyarch
> y"

.                                 local i=1

.                                 foreach t of local dimensions {
  2.                                         local j = 1
  3.                                         local klass = "v2x_liberal v2x_partip v2x
> dl_delib v2x_egal v2x_veracc v2x_horacc xrreg xrcomp xropen xconst parreg parcomp po
> lcomp pr1 pr2 pr3"
  4.                                         foreach k of local klass {
  5.                                                 spearman `t' `k'
  6.                                                 local rho = r(rho)
  7.                                                 local s = round(`rho',.01)
  8.                                                 local f = abs(`s')
  9.                                                 matrix m[`i',`j'] =`f'
 10.                                                 local j= `j' + 1
 11.                                         }
 12.                                         local i = `i'+1
 13.                                 }

 Number of obs =    4548
Spearman's rho =      -0.0028

Test of Ho: pr1 and v2x_liberal are independent
    Prob > |t| =       0.8500

 Number of obs =    4551
Spearman's rho =       0.0743

Test of Ho: pr1 and v2x_partip are independent
    Prob > |t| =       0.0000

 Number of obs =    4551
Spearman's rho =       0.0936

Test of Ho: pr1 and v2xdl_delib are independent
    Prob > |t| =       0.0000

 Number of obs =    4548
Spearman's rho =       0.2901

Test of Ho: pr1 and v2x_egal are independent
    Prob > |t| =       0.0000

 Number of obs =    4496
Spearman's rho =       0.2654

Test of Ho: pr1 and v2x_veracc are independent
    Prob > |t| =       0.0000

 Number of obs =    4496
Spearman's rho =      -0.0066

Test of Ho: pr1 and v2x_horacc are independent
    Prob > |t| =       0.6600

 Number of obs =    4410
Spearman's rho =      -0.1934

Test of Ho: pr1 and xrreg are independent
    Prob > |t| =       0.0000

 Number of obs =    4410
Spearman's rho =       0.1973

Test of Ho: pr1 and xrcomp are independent
    Prob > |t| =       0.0000

 Number of obs =    4410
Spearman's rho =       0.4893

Test of Ho: pr1 and xropen are independent
    Prob > |t| =       0.0000

 Number of obs =    4410
Spearman's rho =       0.1732

Test of Ho: pr1 and xconst are independent
    Prob > |t| =       0.0000

 Number of obs =    4410
Spearman's rho =       0.0156

Test of Ho: pr1 and parreg are independent
    Prob > |t| =       0.2997

 Number of obs =    4410
Spearman's rho =       0.0526

Test of Ho: pr1 and parcomp are independent
    Prob > |t| =       0.0005

 Number of obs =    4410
Spearman's rho =       0.0230

Test of Ho: pr1 and polcomp are independent
    Prob > |t| =       0.1264

 Number of obs =    4591
Spearman's rho =       1.0000

Test of Ho: pr1 and pr1 are independent
    Prob > |t| =       0.0000

 Number of obs =    4591
Spearman's rho =      -0.2335

Test of Ho: pr1 and pr2 are independent
    Prob > |t| =       0.0000

 Number of obs =    4591
Spearman's rho =      -0.1711

Test of Ho: pr1 and pr3 are independent
    Prob > |t| =       0.0000

 Number of obs =    4548
Spearman's rho =      -0.2692

Test of Ho: pr2 and v2x_liberal are independent
    Prob > |t| =       0.0000

 Number of obs =    4551
Spearman's rho =      -0.1096

Test of Ho: pr2 and v2x_partip are independent
    Prob > |t| =       0.0000

 Number of obs =    4551
Spearman's rho =      -0.2708

Test of Ho: pr2 and v2xdl_delib are independent
    Prob > |t| =       0.0000

 Number of obs =    4548
Spearman's rho =      -0.1557

Test of Ho: pr2 and v2x_egal are independent
    Prob > |t| =       0.0000

 Number of obs =    4496
Spearman's rho =      -0.3578

Test of Ho: pr2 and v2x_veracc are independent
    Prob > |t| =       0.0000

 Number of obs =    4496
Spearman's rho =      -0.3651

Test of Ho: pr2 and v2x_horacc are independent
    Prob > |t| =       0.0000

 Number of obs =    4410
Spearman's rho =      -0.5123

Test of Ho: pr2 and xrreg are independent
    Prob > |t| =       0.0000

 Number of obs =    4410
Spearman's rho =      -0.4986

Test of Ho: pr2 and xrcomp are independent
    Prob > |t| =       0.0000

 Number of obs =    4410
Spearman's rho =      -0.2815

Test of Ho: pr2 and xropen are independent
    Prob > |t| =       0.0000

 Number of obs =    4410
Spearman's rho =      -0.2953

Test of Ho: pr2 and xconst are independent
    Prob > |t| =       0.0000

 Number of obs =    4410
Spearman's rho =       0.0505

Test of Ho: pr2 and parreg are independent
    Prob > |t| =       0.0008

 Number of obs =    4410
Spearman's rho =      -0.1594

Test of Ho: pr2 and parcomp are independent
    Prob > |t| =       0.0000

 Number of obs =    4410
Spearman's rho =      -0.1562

Test of Ho: pr2 and polcomp are independent
    Prob > |t| =       0.0000

 Number of obs =    4591
Spearman's rho =      -0.2335

Test of Ho: pr2 and pr1 are independent
    Prob > |t| =       0.0000

 Number of obs =    4591
Spearman's rho =       1.0000

Test of Ho: pr2 and pr2 are independent
    Prob > |t| =       0.0000

 Number of obs =    4591
Spearman's rho =       0.0571

Test of Ho: pr2 and pr3 are independent
    Prob > |t| =       0.0001

 Number of obs =    4548
Spearman's rho =      -0.1720

Test of Ho: pr3 and v2x_liberal are independent
    Prob > |t| =       0.0000

 Number of obs =    4551
Spearman's rho =      -0.1258

Test of Ho: pr3 and v2x_partip are independent
    Prob > |t| =       0.0000

 Number of obs =    4551
Spearman's rho =      -0.1965

Test of Ho: pr3 and v2xdl_delib are independent
    Prob > |t| =       0.0000

 Number of obs =    4548
Spearman's rho =      -0.2846

Test of Ho: pr3 and v2x_egal are independent
    Prob > |t| =       0.0000

 Number of obs =    4496
Spearman's rho =      -0.1087

Test of Ho: pr3 and v2x_veracc are independent
    Prob > |t| =       0.0000

 Number of obs =    4496
Spearman's rho =      -0.1320

Test of Ho: pr3 and v2x_horacc are independent
    Prob > |t| =       0.0000

 Number of obs =    4410
Spearman's rho =       0.1231

Test of Ho: pr3 and xrreg are independent
    Prob > |t| =       0.0000

 Number of obs =    4410
Spearman's rho =      -0.2590

Test of Ho: pr3 and xrcomp are independent
    Prob > |t| =       0.0000

 Number of obs =    4410
Spearman's rho =      -0.3349

Test of Ho: pr3 and xropen are independent
    Prob > |t| =       0.0000

 Number of obs =    4410
Spearman's rho =      -0.3812

Test of Ho: pr3 and xconst are independent
    Prob > |t| =       0.0000

 Number of obs =    4410
Spearman's rho =       0.0754

Test of Ho: pr3 and parreg are independent
    Prob > |t| =       0.0000

 Number of obs =    4410
Spearman's rho =      -0.0737

Test of Ho: pr3 and parcomp are independent
    Prob > |t| =       0.0000

 Number of obs =    4410
Spearman's rho =      -0.0753

Test of Ho: pr3 and polcomp are independent
    Prob > |t| =       0.0000

 Number of obs =    4591
Spearman's rho =      -0.1711

Test of Ho: pr3 and pr1 are independent
    Prob > |t| =       0.0000

 Number of obs =    4591
Spearman's rho =       0.0571

Test of Ho: pr3 and pr2 are independent
    Prob > |t| =       0.0001

 Number of obs =    4591
Spearman's rho =       1.0000

Test of Ho: pr3 and pr3 are independent
    Prob > |t| =       0.0000

 Number of obs =    4511
Spearman's rho =       0.4378

Test of Ho: polity2 and v2x_liberal are independent
    Prob > |t| =       0.0000

 Number of obs =    4513
Spearman's rho =       0.5065

Test of Ho: polity2 and v2x_partip are independent
    Prob > |t| =       0.0000

 Number of obs =    4513
Spearman's rho =       0.5201

Test of Ho: polity2 and v2xdl_delib are independent
    Prob > |t| =       0.0000

 Number of obs =    4511
Spearman's rho =       0.0298

Test of Ho: polity2 and v2x_egal are independent
    Prob > |t| =       0.0451

 Number of obs =    4458
Spearman's rho =       0.4677

Test of Ho: polity2 and v2x_veracc are independent
    Prob > |t| =       0.0000

 Number of obs =    4458
Spearman's rho =       0.3873

Test of Ho: polity2 and v2x_horacc are independent
    Prob > |t| =       0.0000

 Number of obs =    4410
Spearman's rho =      -0.0714

Test of Ho: polity2 and xrreg are independent
    Prob > |t| =       0.0000

 Number of obs =    4410
Spearman's rho =       0.2373

Test of Ho: polity2 and xrcomp are independent
    Prob > |t| =       0.0000

 Number of obs =    4410
Spearman's rho =       0.0494

Test of Ho: polity2 and xropen are independent
    Prob > |t| =       0.0010

 Number of obs =    4410
Spearman's rho =       0.7095

Test of Ho: polity2 and xconst are independent
    Prob > |t| =       0.0000

 Number of obs =    4410
Spearman's rho =      -0.6798

Test of Ho: polity2 and parreg are independent
    Prob > |t| =       0.0000

 Number of obs =    4410
Spearman's rho =       0.7950

Test of Ho: polity2 and parcomp are independent
    Prob > |t| =       0.0000

 Number of obs =    4410
Spearman's rho =       0.8348

Test of Ho: polity2 and polcomp are independent
    Prob > |t| =       0.0000

 Number of obs =    4553
Spearman's rho =       0.0756

Test of Ho: polity2 and pr1 are independent
    Prob > |t| =       0.0000

 Number of obs =    4553
Spearman's rho =      -0.0279

Test of Ho: polity2 and pr2 are independent
    Prob > |t| =       0.0598

 Number of obs =    4553
Spearman's rho =      -0.2495

Test of Ho: polity2 and pr3 are independent
    Prob > |t| =       0.0000

 Number of obs =    4487
Spearman's rho =       0.5493

Test of Ho: v2x_polyarchy and v2x_liberal are independent
    Prob > |t| =       0.0000

 Number of obs =    4487
Spearman's rho =       0.5921

Test of Ho: v2x_polyarchy and v2x_partip are independent
    Prob > |t| =       0.0000

 Number of obs =    4487
Spearman's rho =       0.6403

Test of Ho: v2x_polyarchy and v2xdl_delib are independent
    Prob > |t| =       0.0000

 Number of obs =    4487
Spearman's rho =       0.1967

Test of Ho: v2x_polyarchy and v2x_egal are independent
    Prob > |t| =       0.0000

 Number of obs =    4487
Spearman's rho =       0.8637

Test of Ho: v2x_polyarchy and v2x_veracc are independent
    Prob > |t| =       0.0000

 Number of obs =    4487
Spearman's rho =       0.4529

Test of Ho: v2x_polyarchy and v2x_horacc are independent
    Prob > |t| =       0.0000

 Number of obs =    4307
Spearman's rho =       0.1201

Test of Ho: v2x_polyarchy and xrreg are independent
    Prob > |t| =       0.0000

 Number of obs =    4307
Spearman's rho =       0.3501

Test of Ho: v2x_polyarchy and xrcomp are independent
    Prob > |t| =       0.0000

 Number of obs =    4307
Spearman's rho =       0.2341

Test of Ho: v2x_polyarchy and xropen are independent
    Prob > |t| =       0.0000

 Number of obs =    4307
Spearman's rho =       0.4968

Test of Ho: v2x_polyarchy and xconst are independent
    Prob > |t| =       0.0000

 Number of obs =    4307
Spearman's rho =      -0.4525

Test of Ho: v2x_polyarchy and parreg are independent
    Prob > |t| =       0.0000

 Number of obs =    4307
Spearman's rho =       0.6057

Test of Ho: v2x_polyarchy and parcomp are independent
    Prob > |t| =       0.0000

 Number of obs =    4307
Spearman's rho =       0.6203

Test of Ho: v2x_polyarchy and polcomp are independent
    Prob > |t| =       0.0000

 Number of obs =    4487
Spearman's rho =       0.2969

Test of Ho: v2x_polyarchy and pr1 are independent
    Prob > |t| =       0.0000

 Number of obs =    4487
Spearman's rho =      -0.2883

Test of Ho: v2x_polyarchy and pr2 are independent
    Prob > |t| =       0.0000

 Number of obs =    4487
Spearman's rho =      -0.1389

Test of Ho: v2x_polyarchy and pr3 are independent
    Prob > |t| =       0.0000

.                                 matrix list m

m[5,16]
     c1   c2   c3   c4   c5   c6   c7   c8   c9  c10  c11  c12  c13  c14  c15  c16
r1    0  .07  .09  .29  .27  .01  .19   .2  .49  .17  .02  .05  .02    1  .23  .17
r2  .27  .11  .27  .16  .36  .37  .51   .5  .28   .3  .05  .16  .16  .23    1  .06
r3  .17  .13   .2  .28  .11  .13  .12  .26  .33  .38  .08  .07  .08  .17  .06    1
r4  .44  .51  .52  .03  .47  .39  .07  .24  .05  .71  .68  .79  .83  .08  .03  .25
r5  .55  .59  .64   .2  .86  .45  .12  .35  .23   .5  .45  .61  .62   .3  .29  .14

.                                 plotmatrix, m(m) c(yellow) legend(off) freq  split(0
> (.001)1)  xsize(3) ysize(2) ///
>                                 xlab(1 "Liberal (VDem)" 2 "Participation (VDem)" 3 "
> Deliberative (VDem)" 4 "Egalitarian (VDem)" ///
>                                 5 "Vertical Accountability (VDem)" 6 "Horizontal Acc
> ountability (VDem)"  ///
>                                 7 "xrreg (Pol4)" 8 "xrcomp (Pol4)" 9 "xropen (Pol4)"
>  10 "xconst (Pol4)" 11 "parreg (Pol4)" ///
>                                 12 "parcomp (Pol4)"  13 "polcomp (Pol4)" 14 "Party" 
> 15 "Military" 16 "Personal" ///
>                                 , angle(45) labsize(small))  ylab(0 "Party" -1 "Mili
> tary" -2 "Personal" -3 "Polity2" -4 "VDem Polyarchy") 
WARNING: Tested only for Stata version 15.1 and higher.
Your Stata version 14.2 is not officially supported.
SPLIT 0 .001 .002 .003 .004 .005 .006 .007 .008 .009 .01 .011 .012 .013 .014 .015 .016
>  .017 .018 .019 .02 .021 .022 .023 .024 .025 .026 .027 .028 .029 .03 .031 .032 .033 
> .034 .035 .036 .037 .038 .039 .04 .041 .042 .043 .044 .045 .046 .047 .048 .049 .05 .
> 051 .052 .053 .054 .055 .056 .057 .058 .059 .06 .061 .062 .063 .064 .065 .066 .067 .
> 068 .069 .07 .071 .072 .073 .074 .075 .076 .077 .078 .079 .08 .081 .082 .083 .084 .0
> 85 .086 .087 .088 .089 .09 .091 .092 .093 .094 .095 .096 .097 .098 .099 .1 .101 .102
>  .103 .104 .105 .106 .107 .108 .109 .11 .111 .112 .113 .114 .115 .116 .117 .118 .119
>  .12 .121 .122 .123 .124 .125 .126 .127 .128 .129 .13 .131 .132 .133 .134 .135 .136 
> .137 .138 .139 .14 .141 .142 .143 .144 .145 .146 .147 .148 .149 .15 .151 .152 .153 .
> 154 .155 .156 .157 .158 .159 .16 .161 .162 .163 .164 .165 .166 .167 .168 .169 .17 .1
> 71 .172 .173 .174 .175 .176 .177 .178 .179 .18 .181 .182 .183 .184 .185 .186 .187 .1
> 88 .189 .19 .191 .192 .193 .194 .195 .196 .197 .198 .199 .2 .201 .202 .203 .204 .205
>  .206 .207 .208 .209 .21 .211 .212 .213 .214 .215 .216 .217 .218 .219 .22 .221 .222 
> .223 .224 .225 .226 .227 .228 .229 .23 .231 .232 .233 .234 .235 .236 .237 .238 .239 
> .24 .241 .242 .243 .244 .245 .246 .247 .248 .249 .25 .251 .252 .253 .254 .255 .256 .
> 257 .258 .259 .26 .261 .262 .263 .264 .265 .266 .267 .268 .269 .27 .271 .272 .273 .2
> 74 .275 .276 .277 .278 .279 .28 .281 .282 .283 .284 .285 .286 .287 .288 .289 .29 .29
> 1 .292 .293 .294 .295 .296 .297 .298 .299 .3 .301 .302 .303 .304 .305 .306 .307 .308
>  .309 .31 .311 .312 .313 .314 .315 .316 .317 .318 .319 .32 .321 .322 .323 .324 .325 
> .326 .327 .328 .329 .33 .331 .332 .333 .334 .335 .336 .337 .338 .339 .34 .341 .342 .
> 343 .344 .345 .346 .347 .348 .349 .35 .351 .352 .353 .354 .355 .356 .357 .358 .359 .
> 36 .361 .362 .363 .364 .365 .366 .367 .368 .369 .37 .371 .372 .373 .374 .375 .376 .3
> 77 .378 .379 .38 .381 .382 .383 .384 .385 .386 .387 .388 .389 .39 .391 .392 .393 .39
> 4 .395 .396 .397 .398 .399 .4 .401 .402 .403 .404 .405 .406 .407 .408 .409 .41 .411 
> .412 .413 .414 .415 .416 .417 .418 .419 .42 .421 .422 .423 .424 .425 .426 .427 .428 
> .429 .43 .431 .432 .433 .434 .435 .436 .437 .438 .439 .44 .441 .442 .443 .444 .445 .
> 446 .447 .448 .449 .45 .451 .452 .453 .454 .455 .456 .457 .458 .459 .46 .461 .462 .4
> 63 .464 .465 .466 .467 .468 .469 .47 .471 .472 .473 .474 .475 .476 .477 .478 .479 .4
> 8 .481 .482 .483 .484 .485 .486 .487 .488 .489 .49 .491 .492 .493 .494 .495 .496 .49
> 7 .498 .499 .5 .501 .502 .503 .504 .505 .506 .507 .508 .509 .51 .511 .512 .513 .514 
> .515 .516 .517 .518 .519 .52 .521 .522 .523 .524 .525 .526 .527 .528 .529 .53 .531 .
> 532 .533 .534 .535 .536 .537 .538 .539 .54 .541 .542 .543 .544 .545 .546 .547 .548 .
> 549 .55 .551 .552 .553 .554 .555 .556 .557 .558 .559 .56 .561 .562 .563 .564 .565 .5
> 66 .567 .568 .569 .57 .571 .572 .573 .574 .575 .576 .577 .578 .579 .58 .581 .582 .58
> 3 .584 .585 .586 .587 .588 .589 .59 .591 .592 .593 .594 .595 .596 .597 .598 .599 .6 
> .601 .602 .603 .604 .605 .606 .607 .608 .609 .61 .611 .612 .613 .614 .615 .616 .617 
> .618 .619 .62 .621 .622 .623 .624 .625 .626 .627 .628 .629 .63 .631 .632 .633 .634 .
> 635 .636 .637 .638 .639 .64 .641 .642 .643 .644 .645 .646 .647 .648 .649 .65 .651 .6
> 52 .653 .654 .655 .656 .657 .658 .659 .66 .661 .662 .663 .664 .665 .666 .667 .668 .6
> 69 .67 .671 .672 .673 .674 .675 .676 .677 .678 .679 .68 .681 .682 .683 .684 .685 .68
> 6 .687 .688 .689 .69 .691 .692 .693 .694 .695 .696 .697 .698 .699 .7 .701 .702 .703 
> .704 .705 .706 .707 .708 .709 .71 .711 .712 .713 .714 .715 .716 .717 .718 .719 .72 .
> 721 .722 .723 .724 .725 .726 .727 .728 .729 .73 .731 .732 .733 .734 .735 .736 .737 .
> 738 .739 .74 .741 .742 .743 .744 .745 .746 .747 .748 .749 .75 .751 .752 .753 .754 .7
> 55 .756 .757 .758 .759 .76 .761 .762 .763 .764 .765 .766 .767 .768 .769 .77 .771 .77
> 2 .773 .774 .775 .776 .777 .778 .779 .78 .781 .782 .783 .784 .785 .786 .787 .788 .78
> 9 .79 .791 .792 .793 .794 .795 .796 .797 .798 .799 .8 .801 .802 .803 .804 .805 .806 
> .807 .808 .809 .81 .811 .812 .813 .814 .815 .816 .817 .818 .819 .82 .821 .822 .823 .
> 824 .825 .826 .827 .828 .829 .83 .831 .832 .833 .834 .835 .836 .837 .838 .839 .84 .8
> 41 .842 .843 .844 .845 .846 .847 .848 .849 .85 .851 .852 .853 .854 .855 .856 .857 .8
> 58 .859 .86 .861 .862 .863 .864 .865 .866 .867 .868 .869 .87 .871 .872 .873 .874 .87
> 5 .876 .877 .878 .879 .88 .881 .882 .883 .884 .885 .886 .887 .888 .889 .89 .891 .892
>  .893 .894 .895 .896 .897 .898 .899 .9 .901 .902 .903 .904 .905 .906 .907 .908 .909 
> .91 .911 .912 .913 .914 .915 .916 .917 .918 .919 .92 .921 .922 .923 .924 .925 .926 .
> 927 .928 .929 .93 .931 .932 .933 .934 .935 .936 .937 .938 .939 .94 .941 .942 .943 .9
> 44 .945 .946 .947 .948 .949 .95 .951 .952 .953 .954 .955 .956 .957 .958 .959 .96 .96
> 1 .962 .963 .964 .965 .966 .967 .968 .969 .97 .971 .972 .973 .974 .975 .976 .977 .97
> 8 .979 .98 .981 .982 .983 .984 .985 .986 .987 .988 .989 .99 .991 .992 .993 .994 .995
>  .996 .997 .998 .999 1

.                                 graph export "$dir/golden/CorrDemComponents.pdf",as(
> pdf) replace
(file /Users/lee/Dropbox/Datavers/golden/CorrDemComponents.pdf written in PDF format)

. 
.                                 * Personalist correlation within case with
.                                 use temp,clear

.                                 global unit  ="gwf_caseid"

.                                 local var = "xrreg xrcomp xropen xconst parreg parco
> mp  polcomp"

.                                 foreach v of local var {
  2.                                         recode `v' (-66=.) (-77=.) (-88=.) (-99=.
> )
  3.                                 }
(xrreg: 174 changes made)
(xrcomp: 174 changes made)
(xropen: 174 changes made)
(xconst: 174 changes made)
(parreg: 174 changes made)
(parcomp: 174 changes made)
(polcomp: 174 changes made)

.                                 local vars = "pr1 pr2 pr3 irtpers8 irtgrm"

.                                 foreach i of local vars {
  2.                                         qui xtset $unit year
  3.                                         qui xtsum `i'
  4.                                         scalar sdb`i' = r(sd_b)
  5.                                         scalar sdw`i' = r(sd_w)
  6.                                         scalar vart`i'= sdb`i' + sdw`i'
  7.                                         scalar varr`i' = sdw`i' / vart`i'
  8.                                         scalar list sdw`i'
  9.                                         scalar list varr`i'
 10.                                         qui reg `i' i.$unit
 11.                                         qui predict within_`i', resid
 12.                                 }                
    sdwpr1 =   .3566731
   varrpr1 =  .27748675
    sdwpr2 =  .25613539
   varrpr2 =  .18952974
    sdwpr3 =  .40595812
   varrpr3 =  .33066178
sdwirtpers8 =  .46633623
varrirtpers8 =  .37596609
 sdwirtgrm =  .41940402
varrirtgrm =   .3357457

.                                 matrix m = J(16,2,.)

.                                 matrix rownames m = liberal partip delib egal veracc
>  horacc dissolve veto dismiss xrreg xrcomp xropen xconst parreg parcomp polcomp

.                                 local j = 1

.                                 local vars = "v2x_liberal v2x_partip v2xdl_delib v2x
> _egal v2x_veracc v2x_horacc v2exdfdshs  v2exdfvths v2exdfdmhs xrreg xrcomp xropen xc
> onst parreg parcomp polcomp"

.                                 foreach i of local vars {
  2.                                         qui xtset $unit year
  3.                                         qui xtsum `i'
  4.                                         scalar sdb`i' = r(sd_b)
  5.                                         scalar sdw`i' = r(sd_w)
  6.                                         scalar vart`i'= sdb`i' + sdw`i'
  7.                                         scalar varr`i' = sdw`i' / vart`i'
  8.                                         local swd=varr`i'
  9.                                         local s = round(`swd',.001)
 10.                                         local f = (`s')
 11.                                         matrix m[`j',1] =`f'
 12.                                         qui reg `i' i.$unit
 13.                                         qui predict within_`i', resid
 14.                                         qui spearman within_irtpers8 within_`i'
 15.                                         local rho = r(rho)
 16.                                         local s = round(`rho',.001)
 17.                                         local f = (`s')
 18.                                         matrix m[`j',2] =`f'
 19.                                         local j= `j' + 1
 20.                                 }                

.                                 **** The contents of the following matrix is Table B
> -2 output ****
.                                 matrix list m

m[16,2]
             c1     c2
 liberal   .274  -.174
  partip   .283  -.032
   delib   .323  -.129
    egal   .197   .086
  veracc   .417   .006
  horacc   .286  -.157
dissolve   .243   .183
    veto   .237   .163
 dismiss   .224   .157
   xrreg   .278   .035
  xrcomp   .246  -.089
  xropen   .231   .033
  xconst   .292  -.108
  parreg   .367    .04
 parcomp   .361  -.041
 polcomp   .368  -.054

. 
.  
.                 ***************************************
.                 *** Detailed comparison with Polity ***
.                 ***************************************
.                 use temp, clear

.                 set more off

.                 tab polity2

    polity2 |      Freq.     Percent        Cum.
------------+-----------------------------------
        -10 |        211        4.63        4.63
         -9 |        684       15.02       19.66
         -8 |        345        7.58       27.23
         -7 |      1,394       30.62       57.85
         -6 |        405        8.90       66.75
         -5 |        190        4.17       70.92
         -4 |        177        3.89       74.81
         -3 |        194        4.26       79.07
         -2 |        155        3.40       82.47
         -1 |        162        3.56       86.03
          0 |        110        2.42       88.45
          1 |         65        1.43       89.87
          2 |         55        1.21       91.08
          3 |         62        1.36       92.44
          4 |         96        2.11       94.55
          5 |         78        1.71       96.27
          6 |         66        1.45       97.72
          7 |         42        0.92       98.64
          8 |         44        0.97       99.60
          9 |          5        0.11       99.71
         10 |         13        0.29      100.00
------------+-----------------------------------
      Total |      4,553      100.00

.                 gen pol4  = polity2 +10
(38 missing values generated)

.                 sum pol4

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
        pol4 |      4,553    4.822315    4.265086          0         20

.                 gen c =.
(4,591 missing values generated)

.                 gen n =.
(4,591 missing values generated)

.                 gen sd1 = .
(4,591 missing values generated)

.                 gen mean1 =.
(4,591 missing values generated)

.                 gen sd2 = .
(4,591 missing values generated)

.                 gen mean2 =.
(4,591 missing values generated)

.                 gen sd3 = .
(4,591 missing values generated)

.                 gen mean3 =.
(4,591 missing values generated)

.                 forval d = 1/3 {
  2.                         forval i = 1/21 {
  3.                                 replace c = `i' if _n==`i'
  4.                                 sum pr`d' if pol4==`i'
  5.                                 replace n = r(N) if _n==`i'
  6.                                 replace sd`d'= r(sd)  if _n==`i'
  7.                                 replace mean`d' = r(mean) if _n==`i'    
  8.                         }
  9.                 }
(1 real change made)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
         pr1 |        684    .1509245    1.045136  -2.027763   1.285902
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
         pr1 |        345    .0095082    1.082656  -1.981931   1.117453
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
         pr1 |      1,394    .0543506    .8978744  -2.093561   1.139001
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
         pr1 |        405    .0698336    .9879787  -1.967983     1.2687
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
         pr1 |        190   -.1544014    .9837517  -2.027763   1.156047
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
         pr1 |        177    .1163344    .9492867  -2.073113   1.006521
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
         pr1 |        194   -.0225049    .8760572  -2.037319   1.011179
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
         pr1 |        155    .1731079    .9397088  -2.049522   .8967866
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
         pr1 |        162   -.0475523    1.002885  -2.034881   .8830694
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
         pr1 |        110    -.156322    .8687721  -1.541283   1.183908
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
         pr1 |         65     .231903     .724454  -1.504516     .84963
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
         pr1 |         55   -.0505069    .9038198  -1.923582   .7692415
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
         pr1 |         62   -.2910915     .904573   -1.45823   .8401898
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
         pr1 |         96    .4371138    .4849168  -1.352435    .883557
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
         pr1 |         78    .1620379    .7281196  -1.909606   .6877094
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
         pr1 |         66    .4740771    .7009399  -1.514502   1.039532
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
         pr1 |         42     .199313    .9786204  -1.550815   .9720964
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
         pr1 |         44    .4972678    .7049977  -1.457034   .9720964
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
         pr1 |          5    .4779764    .0482181    .399861   .5329214
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
         pr1 |         13    .3834459    .5087196   -1.29712   .6595868
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
         pr1 |          0
(1 real change made)
(0 real changes made)
(0 real changes made)
(0 real changes made)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
         pr2 |        684   -.2509464    .8154286  -1.443774   2.137978
(0 real changes made)
(1 real change made)
(1 real change made)
(0 real changes made)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
         pr2 |        345    .0929906    .9447553  -1.200279   1.985695
(0 real changes made)
(1 real change made)
(1 real change made)
(0 real changes made)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
         pr2 |      1,394    .3263154    1.031156  -1.316814   2.034857
(0 real changes made)
(1 real change made)
(1 real change made)
(0 real changes made)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
         pr2 |        405    .0040135     .999259  -1.366134   1.989591
(0 real changes made)
(1 real change made)
(1 real change made)
(0 real changes made)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
         pr2 |        190    .5157959    1.008165  -1.423467   1.974185
(0 real changes made)
(1 real change made)
(1 real change made)
(0 real changes made)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
         pr2 |        177   -.1132098    .9201926  -1.139356   1.974185
(0 real changes made)
(1 real change made)
(1 real change made)
(0 real changes made)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
         pr2 |        194    .0986244    .9661415  -1.278524   1.993877
(0 real changes made)
(1 real change made)
(1 real change made)
(0 real changes made)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
         pr2 |        155   -.2829266    .9144291  -1.322397   1.970075
(0 real changes made)
(1 real change made)
(1 real change made)
(0 real changes made)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
         pr2 |        162    .0744537    1.050244  -1.198322    1.92185
(0 real changes made)
(1 real change made)
(1 real change made)
(0 real changes made)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
         pr2 |        110    .0844294    1.018893  -1.408748   1.945267
(0 real changes made)
(1 real change made)
(1 real change made)
(0 real changes made)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
         pr2 |         65   -.2478808    .8658478  -1.154743   1.755019
(0 real changes made)
(1 real change made)
(1 real change made)
(0 real changes made)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
         pr2 |         55    .2553268    1.098178   -1.41626   1.960027
(0 real changes made)
(1 real change made)
(1 real change made)
(0 real changes made)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
         pr2 |         62    .1753038    1.195824  -1.107296   1.970075
(0 real changes made)
(1 real change made)
(1 real change made)
(0 real changes made)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
         pr2 |         96   -.8798604    .3539276  -1.131616   1.628922
(0 real changes made)
(1 real change made)
(1 real change made)
(0 real changes made)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
         pr2 |         78   -.5362871    .6055189  -1.115197   1.248354
(0 real changes made)
(1 real change made)
(1 real change made)
(0 real changes made)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
         pr2 |         66   -.6440353    .8211408  -1.275741    1.73923
(0 real changes made)
(1 real change made)
(1 real change made)
(0 real changes made)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
         pr2 |         42   -.4569467    1.009877  -1.400076   1.888395
(0 real changes made)
(1 real change made)
(1 real change made)
(0 real changes made)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
         pr2 |         44   -.7025804    .8991867  -1.411281   1.726607
(0 real changes made)
(1 real change made)
(1 real change made)
(0 real changes made)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
         pr2 |          5   -.5185417    .9511619  -1.032206    1.15748
(0 real changes made)
(1 real change made)
(1 real change made)
(0 real changes made)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
         pr2 |         13   -1.099964    .2564897  -1.212585  -.2777287
(0 real changes made)
(1 real change made)
(1 real change made)
(0 real changes made)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
         pr2 |          0
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
         pr3 |        684    .3815721    .8840423  -2.001452   1.766379
(0 real changes made)
(1 real change made)
(1 real change made)
(0 real changes made)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
         pr3 |        345     .206017    .8429997  -1.892668   1.724189
(0 real changes made)
(1 real change made)
(1 real change made)
(0 real changes made)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
         pr3 |      1,394   -.1815787    1.122698  -2.265968   2.247444
(0 real changes made)
(1 real change made)
(1 real change made)
(0 real changes made)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
         pr3 |        405   -.0099542    .8551789  -2.052052   1.545069
(0 real changes made)
(1 real change made)
(1 real change made)
(0 real changes made)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
         pr3 |        190     .017918    .9462743  -1.916513   1.894881
(0 real changes made)
(1 real change made)
(1 real change made)
(0 real changes made)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
         pr3 |        177    .2189509     .754004  -1.853618   1.781071
(0 real changes made)
(1 real change made)
(1 real change made)
(0 real changes made)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
         pr3 |        194   -.1578551    .8491916  -1.755733   1.894881
(0 real changes made)
(1 real change made)
(1 real change made)
(0 real changes made)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
         pr3 |        155    .4482738    1.106749  -1.751889    2.35445
(0 real changes made)
(1 real change made)
(1 real change made)
(0 real changes made)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
         pr3 |        162   -.2691112     1.01418  -1.331732    1.64661
(0 real changes made)
(1 real change made)
(1 real change made)
(0 real changes made)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
         pr3 |        110    .0358265     .933543  -1.853618   1.382801
(0 real changes made)
(1 real change made)
(1 real change made)
(0 real changes made)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
         pr3 |         65   -.0680184    .6105106  -1.477516   1.533181
(0 real changes made)
(1 real change made)
(1 real change made)
(0 real changes made)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
         pr3 |         55    -.409516    .6166557   -1.29628   .9492926
(0 real changes made)
(1 real change made)
(1 real change made)
(0 real changes made)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
         pr3 |         62   -.2402583     .675941  -1.247501   1.474826
(0 real changes made)
(1 real change made)
(1 real change made)
(0 real changes made)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
         pr3 |         96   -.4911034    .5523802   -1.77544   1.217064
(0 real changes made)
(1 real change made)
(1 real change made)
(0 real changes made)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
         pr3 |         78   -.5523008    .9553987  -1.886325   1.861862
(0 real changes made)
(1 real change made)
(1 real change made)
(0 real changes made)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
         pr3 |         66   -.6410675     .645387  -1.773978   1.396671
(0 real changes made)
(1 real change made)
(1 real change made)
(0 real changes made)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
         pr3 |         42   -.7990324    .5814479  -1.681791    .295308
(0 real changes made)
(1 real change made)
(1 real change made)
(0 real changes made)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
         pr3 |         44   -.4621682    .5835104  -1.411335   .7948526
(0 real changes made)
(1 real change made)
(1 real change made)
(0 real changes made)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
         pr3 |          5    -1.21472    1.012394  -1.773482   .5610141
(0 real changes made)
(1 real change made)
(1 real change made)
(0 real changes made)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
         pr3 |         13   -.6478876    .1104339  -.7040141  -.3556856
(0 real changes made)
(1 real change made)
(1 real change made)
(0 real changes made)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
         pr3 |          0
(0 real changes made)
(0 real changes made)
(0 real changes made)

.                 replace c = c-11
(21 real changes made)

.                 twoway (bar n c,yaxis(2) color(gs14) xtitle("Polity score") ytitle("
>  ", axis(2))/*
>                 */ legend(lab(2 "Party") lab(3 "Military") lab(4 "Personal") lab(1 "
> # obs") /*
>                 */ pos(12) ring(1) col(4))) (line mean1 c, color(blue) yline(0, lpat
> tern(dash))) /*
>                 */ (line mean2 c,color(green)ylab(,glcol(gs15)))  (line mean3 c, col
> or(red) ytitle(Mean)) 

.                 graph export "$dir/golden/PolMeans.pdf", as(pdf)  replace
(file /Users/lee/Dropbox/Datavers/golden/PolMeans.pdf written in PDF format)

. 
.                 label var $d1 " "

.                 label var $d2 " "

.                 label var $d3 " "

.                 forval z =-10/10 {
  2.                         twoway (scatter $d2 $d1 if polity2~=`z' & gwf_fail~=., ti
> tle("`z'") msymbol(circle) mfcolor(gs16) mcolor(gs12) saving(`z', replace) ) /*
>                         */ (scatter $d2 $d1 if polity2==`z', xtitle("") ytitle("")  
> msymbol(circle) mcolor(red) mfcolor(gs16) scheme(lean1)  legend(off)   /*
>                         */ yscale(range (-2 2)) ylabel(-2 (1) 2,  glcolor(gs14))  xs
> cale(range (-2 2)) xlabel(-2 (1) 2,  glcolor(gs14)) )
  3.                 }
(note: scheme lean1 not found, using s2color)
(note: file -10.gph not found)
(file -10.gph saved)
(note: scheme lean1 not found, using s2color)
(note: file -9.gph not found)
(file -9.gph saved)
(note: scheme lean1 not found, using s2color)
(note: file -8.gph not found)
(file -8.gph saved)
(note: scheme lean1 not found, using s2color)
(note: file -7.gph not found)
(file -7.gph saved)
(note: scheme lean1 not found, using s2color)
(note: file -6.gph not found)
(file -6.gph saved)
(note: scheme lean1 not found, using s2color)
(note: file -5.gph not found)
(file -5.gph saved)
(note: scheme lean1 not found, using s2color)
(note: file -4.gph not found)
(file -4.gph saved)
(note: scheme lean1 not found, using s2color)
(note: file -3.gph not found)
(file -3.gph saved)
(note: scheme lean1 not found, using s2color)
(note: file -2.gph not found)
(file -2.gph saved)
(note: scheme lean1 not found, using s2color)
(note: file -1.gph not found)
(file -1.gph saved)
(note: scheme lean1 not found, using s2color)
(note: file 0.gph not found)
(file 0.gph saved)
(note: scheme lean1 not found, using s2color)
(note: file 1.gph not found)
(file 1.gph saved)
(note: scheme lean1 not found, using s2color)
(note: file 2.gph not found)
(file 2.gph saved)
(note: scheme lean1 not found, using s2color)
(note: file 3.gph not found)
(file 3.gph saved)
(note: scheme lean1 not found, using s2color)
(note: file 4.gph not found)
(file 4.gph saved)
(note: scheme lean1 not found, using s2color)
(note: file 5.gph not found)
(file 5.gph saved)
(note: scheme lean1 not found, using s2color)
(note: file 6.gph not found)
(file 6.gph saved)
(note: scheme lean1 not found, using s2color)
(note: file 7.gph not found)
(file 7.gph saved)
(note: scheme lean1 not found, using s2color)
(note: file 8.gph not found)
(file 8.gph saved)
(note: scheme lean1 not found, using s2color)
(note: file 9.gph not found)
(file 9.gph saved)
(note: scheme lean1 not found, using s2color)
(note: file 10.gph not found)
(file 10.gph saved)

.                 gr combine -10.gph -9.gph -8.gph -7.gph -6.gph -5.gph -4.gph -3.gph 
> -2.gph -1.gph 0.gph 1.gph 2.gph 3.gph 4.gph , col(3) ysize(8) l1(Military) b1(Party)

.                 graph export "$dir/golden/D1D2-by-polity.pdf", as(pdf)  replace
(file /Users/lee/Dropbox/Datavers/golden/D1D2-by-polity.pdf written in PDF format)

.                 forval z =-10/10 {
  2.                         twoway (scatter $d3 $d2 if polity2~=`z' & gwf_fail~=., ti
> tle("`z'") msymbol(circle) mfcolor(gs16) mcolor(gs12) saving(`z', replace) ) /*
>                         */ (scatter $d3 $d2 if polity2==`z', xtitle("") ytitle("")  
> msymbol(circle) mcolor(red) mfcolor(gs16) scheme(lean1)  legend(off)   /*
>                         */ yscale(range (-2 2)) ylabel(-2 (1) 2,  glcolor(gs14))  xs
> cale(range (-2 2)) xlabel(-2 (1) 2,  glcolor(gs14)) )
  3.                 }
(note: scheme lean1 not found, using s2color)
(file -10.gph saved)
(note: scheme lean1 not found, using s2color)
(file -9.gph saved)
(note: scheme lean1 not found, using s2color)
(file -8.gph saved)
(note: scheme lean1 not found, using s2color)
(file -7.gph saved)
(note: scheme lean1 not found, using s2color)
(file -6.gph saved)
(note: scheme lean1 not found, using s2color)
(file -5.gph saved)
(note: scheme lean1 not found, using s2color)
(file -4.gph saved)
(note: scheme lean1 not found, using s2color)
(file -3.gph saved)
(note: scheme lean1 not found, using s2color)
(file -2.gph saved)
(note: scheme lean1 not found, using s2color)
(file -1.gph saved)
(note: scheme lean1 not found, using s2color)
(file 0.gph saved)
(note: scheme lean1 not found, using s2color)
(file 1.gph saved)
(note: scheme lean1 not found, using s2color)
(file 2.gph saved)
(note: scheme lean1 not found, using s2color)
(file 3.gph saved)
(note: scheme lean1 not found, using s2color)
(file 4.gph saved)
(note: scheme lean1 not found, using s2color)
(file 5.gph saved)
(note: scheme lean1 not found, using s2color)
(file 6.gph saved)
(note: scheme lean1 not found, using s2color)
(file 7.gph saved)
(note: scheme lean1 not found, using s2color)
(file 8.gph saved)
(note: scheme lean1 not found, using s2color)
(file 9.gph saved)
(note: scheme lean1 not found, using s2color)
(file 10.gph saved)

.                 gr combine -10.gph -9.gph -8.gph -7.gph -6.gph -5.gph -4.gph -3.gph 
> -2.gph -1.gph 0.gph 1.gph 2.gph 3.gph 4.gph , col(3) ysize(8) l1(Personalism) b1(Mil
> itary)

.                 graph export "$dir/golden/D2D3-by-polity.pdf", as(pdf)  replace
(file /Users/lee/Dropbox/Datavers/golden/D2D3-by-polity.pdf written in PDF format)

.                 forval z =-10/10 {
  2.                         twoway (scatter $d3 $d1 if polity2~=`z' & gwf_fail~=., ti
> tle("`z'") msymbol(circle) mfcolor(gs16) mcolor(gs12) saving(`z', replace) ) /*
>                         */ (scatter $d3 $d1 if polity2==`z', xtitle("") ytitle("")  
> msymbol(circle) mcolor(red) mfcolor(gs16) scheme(lean1)  legend(off)   /*
>                         */ yscale(range (-2 2)) ylabel(-2 (1) 2,  glcolor(gs14))  xs
> cale(range (-2 2)) xlabel(-2 (1) 2,  glcolor(gs14)) )
  3.                 }
(note: scheme lean1 not found, using s2color)
(file -10.gph saved)
(note: scheme lean1 not found, using s2color)
(file -9.gph saved)
(note: scheme lean1 not found, using s2color)
(file -8.gph saved)
(note: scheme lean1 not found, using s2color)
(file -7.gph saved)
(note: scheme lean1 not found, using s2color)
(file -6.gph saved)
(note: scheme lean1 not found, using s2color)
(file -5.gph saved)
(note: scheme lean1 not found, using s2color)
(file -4.gph saved)
(note: scheme lean1 not found, using s2color)
(file -3.gph saved)
(note: scheme lean1 not found, using s2color)
(file -2.gph saved)
(note: scheme lean1 not found, using s2color)
(file -1.gph saved)
(note: scheme lean1 not found, using s2color)
(file 0.gph saved)
(note: scheme lean1 not found, using s2color)
(file 1.gph saved)
(note: scheme lean1 not found, using s2color)
(file 2.gph saved)
(note: scheme lean1 not found, using s2color)
(file 3.gph saved)
(note: scheme lean1 not found, using s2color)
(file 4.gph saved)
(note: scheme lean1 not found, using s2color)
(file 5.gph saved)
(note: scheme lean1 not found, using s2color)
(file 6.gph saved)
(note: scheme lean1 not found, using s2color)
(file 7.gph saved)
(note: scheme lean1 not found, using s2color)
(file 8.gph saved)
(note: scheme lean1 not found, using s2color)
(file 9.gph saved)
(note: scheme lean1 not found, using s2color)
(file 10.gph saved)

.                 gr combine -10.gph -9.gph -8.gph -7.gph -6.gph -5.gph -4.gph -3.gph 
> -2.gph -1.gph 0.gph 1.gph 2.gph 3.gph 4.gph , col(3) ysize(8) l1(Personalism) b1(Par
> ty)

.                 graph export "$dir/golden/D1D3-by-polity.pdf", as(pdf)  replace
(file /Users/lee/Dropbox/Datavers/golden/D1D3-by-polity.pdf written in PDF format)

. 
.                 * Erase stored .gph files
.                 local var  = "caseid leadid Personalist Party Monarchy Military"

.                 foreach v of local var {
  2.                         erase `v'.gph
  3.                 }

.                 forval i=-10(1)10 {
  2.                         erase `i'.gph
  3.                 }

.  
.  ***********************************************************************************
> ****************
.  ************ RE ANALYIS of WEEKS 2014 Dictators at War and Peace, Chapter 2: MID In
> itiation *******
.  ***********************************************************************************
> ****************
.                 * Construct GWF personalism measures *
.                 use "$dir/temp.dta",clear

.                 irt (2pl sectyapppers officepers partyrbr partyexcompers paramilpers
>  milmeritpers milnotrial milconsult heirfam heirclan)

Fitting fixed-effects model:

Iteration 0:   log likelihood = -28868.637  
Iteration 1:   log likelihood =  -28819.41  
Iteration 2:   log likelihood = -28819.391  
Iteration 3:   log likelihood = -28819.391  

Fitting full model:

Iteration 0:   log likelihood = -26446.801  
Iteration 1:   log likelihood =  -25622.64  
Iteration 2:   log likelihood = -25570.201  
Iteration 3:   log likelihood = -25567.715  
Iteration 4:   log likelihood = -25567.694  
Iteration 5:   log likelihood = -25567.693  

Two-parameter logistic model                    Number of obs     =      4,591
Log likelihood = -25567.693
------------------------------------------------------------------------------
             |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
sectyapppers |
     Discrim |   2.210456   .0998394    22.14   0.000     2.014775    2.406138
        Diff |  -.2951619   .0244253   -12.08   0.000    -.3430346   -.2472893
-------------+----------------------------------------------------------------
officepers   |
     Discrim |   3.152781   .1726503    18.26   0.000     2.814393     3.49117
        Diff |  -.4050003   .0226251   -17.90   0.000    -.4493446   -.3606559
-------------+----------------------------------------------------------------
partyrbrstmp |
     Discrim |   1.325102   .0695969    19.04   0.000     1.188695     1.46151
        Diff |   .8371441   .0410965    20.37   0.000     .7565964    .9176919
-------------+----------------------------------------------------------------
partye~mpers |
     Discrim |   1.473219   .0762869    19.31   0.000       1.3237    1.622739
        Diff |   .7252776   .0359551    20.17   0.000     .6548069    .7957482
-------------+----------------------------------------------------------------
paramilpers  |
     Discrim |   1.495339   .0714467    20.93   0.000     1.355306    1.635372
        Diff |    .572165   .0321075    17.82   0.000     .5092354    .6350945
-------------+----------------------------------------------------------------
milmeritpers |
     Discrim |   1.433177   .0629595    22.76   0.000     1.309779    1.556576
        Diff |   .3060044   .0295242    10.36   0.000      .248138    .3638708
-------------+----------------------------------------------------------------
milnotrial   |
     Discrim |   1.435678   .0643599    22.31   0.000     1.309535    1.561821
        Diff |   .5362201   .0320692    16.72   0.000     .4733657    .5990745
-------------+----------------------------------------------------------------
milconsult   |
     Discrim |  -.8860233   .0594758   -14.90   0.000    -1.002594   -.7694528
        Diff |  -2.400114   .1360143   -17.65   0.000    -2.666697   -2.133531
-------------+----------------------------------------------------------------
heirfamily   |
     Discrim |   1.139533   .0564564    20.18   0.000      1.02888    1.250185
        Diff |   .4871262    .036606    13.31   0.000     .4153798    .5588725
-------------+----------------------------------------------------------------
heirclan     |
     Discrim |   .5016776   .0397474    12.62   0.000     .4237743     .579581
        Diff |   1.096769   .1007377    10.89   0.000     .8993268    1.294211
------------------------------------------------------------------------------

.                 predict irtweeks, latent
(option ebmeans assumed)
(using 7 quadrature points)

.                 irtgraph iif,  

.                 graph export "$dir/golden/IIF-G-pers-W.pdf", as(pdf) replace
(file /Users/lee/Dropbox/Datavers/golden/IIF-G-pers-W.pdf written in PDF format)

.                 gsem (PER1-> paramilpers sectyapppers milnotrial milmeritpers, logit
>  var(PER1@1)) ///
>                         (PER2-> officepers partyrbr create partyexcompers, logit var
> (PER2@1))

Fitting fixed-effects model:

Iteration 0:   log likelihood = -22969.605  
Iteration 1:   log likelihood =  -22931.47  
Iteration 2:   log likelihood = -22931.455  
Iteration 3:   log likelihood = -22931.455  

Refining starting values:

Grid node 0:   log likelihood = -21262.207

Fitting full model:

Iteration 0:   log likelihood = -21262.207  
Iteration 1:   log likelihood = -20109.812  
Iteration 2:   log likelihood = -19709.711  
Iteration 3:   log likelihood = -19578.404  
Iteration 4:   log likelihood = -19563.796  
Iteration 5:   log likelihood = -19561.648  
Iteration 6:   log likelihood = -19561.585  
Iteration 7:   log likelihood = -19561.582  
Iteration 8:   log likelihood = -19561.581  

Generalized structural equation model           Number of obs     =      4,591

Response       : paramilpers
Family         : Bernoulli
Link           : logit

Response       : sectyapppers
Family         : Bernoulli
Link           : logit

Response       : milnotrial
Family         : Bernoulli
Link           : logit

Response       : milmeritpers
Family         : Bernoulli
Link           : logit

Response       : officepers
Family         : Bernoulli
Link           : logit

Response       : partyrbrstmp
Family         : Bernoulli
Link           : logit

Response       : createparty
Family         : Bernoulli
Link           : logit

Response       : partyexcompers
Family         : Bernoulli
Link           : logit

Log likelihood = -19561.581

 ( 1)  [var(PER1)]_cons = 1
 ( 2)  [var(PER2)]_cons = 1
-----------------------------------------------------------------------------------
                  |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
------------------+----------------------------------------------------------------
paramilpers <-    |
             PER1 |   1.389493   .0712608    19.50   0.000     1.249824    1.529161
            _cons |  -.8236423   .0453056   -18.18   0.000    -.9124396    -.734845
------------------+----------------------------------------------------------------
sectyapppers <-   |
             PER1 |    1.99645   .1107672    18.02   0.000      1.77935     2.21355
            _cons |   .6241474   .0525366    11.88   0.000     .5211776    .7271171
------------------+----------------------------------------------------------------
milnotrial <-     |
             PER1 |   2.208968   .1252606    17.63   0.000     1.963462    2.454474
            _cons |  -.9742235   .0630774   -15.44   0.000    -1.097853    -.850594
------------------+----------------------------------------------------------------
milmeritpers <-   |
             PER1 |    1.83324    .094536    19.39   0.000     1.647953    2.018528
            _cons |  -.4953894   .0487512   -10.16   0.000      -.59094   -.3998387
------------------+----------------------------------------------------------------
officepers <-     |
             PER2 |   2.242072   .1126105    19.91   0.000      2.02136    2.462785
            _cons |   1.023902   .0614465    16.66   0.000     .9034695    1.144335
------------------+----------------------------------------------------------------
partyrbrstmp <-   |
             PER2 |   3.802176   .2326399    16.34   0.000      3.34621    4.258142
            _cons |  -2.138857   .1305058   -16.39   0.000    -2.394644    -1.88307
------------------+----------------------------------------------------------------
createparty <-    |
             PER2 |   1.071572   .0621613    17.24   0.000     .9497381    1.193406
            _cons |  -1.982481   .0571999   -34.66   0.000    -2.094591   -1.870371
------------------+----------------------------------------------------------------
partyexcompers <- |
             PER2 |   4.456161   .3420718    13.03   0.000     3.785713     5.12661
            _cons |  -2.244828   .1706354   -13.16   0.000    -2.579267   -1.910389
------------------+----------------------------------------------------------------
         var(PER1)|          1  (constrained)
         var(PER2)|          1  (constrained)
------------------+----------------------------------------------------------------
    cov(PER2,PER1)|   .6672417   .0158398    42.12   0.000     .6361962    .6982872
-----------------------------------------------------------------------------------

.                 predict gsemMil gsemParty, latent
(option ebmeans assumed)
(using 7 quadrature points)

.                 gen xpers = irtpers8

.                 local var  ="irtweeks irtpers11 irtpers10 irtgrm xpers pr3 gsemMil g
> semParty"

.                 foreach v of local var {
  2.                         sum `v'
  3.                         replace `v' = (`v' +abs(r(min)))/(abs(r(min)) + r(max))
  4.                         sum `v' 
  5.                         hist `v' 
  6.                  }

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
    irtweeks |      4,591    .0013518    .8759115  -1.622126   1.813139
(4,591 real changes made)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
    irtweeks |      4,591    .4725916    .2549764          0          1
(bin=36, start=0, width=.02777778)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
   irtpers11 |      4,591    .0012538    .8853464  -1.473717   2.039324
(4,591 real changes made)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
   irtpers11 |      4,591    .4198558    .2520171          0          1
(bin=36, start=0, width=.02777778)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
   irtpers10 |      4,591    .0014942     .883625   -1.43237   1.966197
(4,591 real changes made)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
   irtpers10 |      4,591    .4219026    .2599993          0          1
(bin=36, start=0, width=.02777778)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
      irtgrm |      4,591    .0005871    .8867916  -1.577697   1.967187
(4,591 real changes made)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
      irtgrm |      4,591    .4452288     .250161          0          1
(bin=36, start=0, width=.02777778)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
       xpers |      4,591    .0014422    .8725584  -1.321256   1.828571
(4,591 real changes made)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
       xpers |      4,591    .4199273    .2770179          0          1
(bin=36, start=0, width=.02777778)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
         pr3 |      4,591    7.64e-10     .982823  -2.265968    2.35445
(4,591 real changes made)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
         pr3 |      4,591    .4904249     .212713          0          1
(bin=36, start=0, width=.02777778)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
     gsemMil |      4,591    .0000768    .8342674   -1.24721   1.610162
(4,591 real changes made)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
     gsemMil |      4,591    .4365153    .2919702          0          1
(bin=36, start=0, width=.02777778)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
   gsemParty |      4,591   -.0003797    .8569156  -1.202676   1.675745
(4,591 real changes made)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
   gsemParty |      4,591    .4176929    .2977033          0          1
(bin=36, start=0, width=.02777778)

.                 replace cow=679 if cow==678 & year>=1991
(20 real changes made)

.                 sort cow year

.                 save "$dir/temp-Weeks.dta",replace
(note: file /Users/lee/Dropbox/Datavers/temp-Weeks.dta not found)
file /Users/lee/Dropbox/Datavers/temp-Weeks.dta saved

.                 
.                 * Merge data sets *
.                 use "$dir/Chapter2-MIDinitiation.dta",clear

.                 gen cowcode = ccode1

.                 sort cow year

.                 merge cow year using "$dir/temp-Weeks.dta"
(note: you are using old merge syntax; see [D] merge for new syntax)
variables cowcode year do not uniquely identify observations in the master data
(note: variable cowcode was float, now double to accommodate using data's values)
(note: variable year was int, now double to accommodate using data's values)

.                 tab _merge

     _merge |      Freq.     Percent        Cum.
------------+-----------------------------------
          1 |    517,195       47.88       47.88
          2 |        626        0.06       47.94
          3 |    562,411       52.06      100.00
------------+-----------------------------------
      Total |  1,080,232      100.00

.                 tab gwf_country if _merge==2 & year<2001

          Country name |      Freq.     Percent        Cum.
-----------------------+-----------------------------------
          Germany East |          4       13.79       13.79
                  Oman |         25       86.21      100.00
-----------------------+-----------------------------------
                 Total |         29      100.00

.                 list cow year if _merge==2 & year<2001 & gwf_country=="Germany East"
>   /* for some reason the Weeks data do not include E Germany from 1950 to 1953 */

         +----------------+
         | cowcode   year |
         |----------------|
1079626. |     265   1950 |
1079627. |     265   1951 |
1079628. |     265   1952 |
1079629. |     265   1953 |
         +----------------+

.                 list ccode year if _merge==1 & year<2001 & abbrev1=="OMA"  /* no Wee
> ks observations for Oman prior to 1970 */

.                 drop if _merge==2
(626 observations deleted)

.                 drop _merge

.                 
.                 * Create variables *
.                 gen time=pcyrsmzinit/100

.                 gen time2=time*time

.                 gen time3=time*time*time

.                 rename persrat_lag_2014_1 persrat_2014_1_lag

.                 rename milrat_lag_2014_1 milrat_2014_1_lag

.                 gen pers= persrat_2014_1_lag    
(709,962 missing values generated)

.                 local var = "pers xpers irtweeks irtpers11 irtpers10 irtgrm pr3 gsem
> Mil gsemParty"

.                 foreach v of local var {
  2.                         gen `v'xmil_lag=`v'*milrat_2014_1_lag
  3.                 }
(731,542 missing values generated)
(674,631 missing values generated)
(674,631 missing values generated)
(674,631 missing values generated)
(674,631 missing values generated)
(674,631 missing values generated)
(674,631 missing values generated)
(674,631 missing values generated)
(674,631 missing values generated)

.                 xtset dirdyadid year
       panel variable:  dirdyadid (unbalanced)
        time variable:  year, 1945 to 2000, but with gaps
                delta:  1 unit

.                 save "$dir/temp-Weeks.dta",replace
file /Users/lee/Dropbox/Datavers/temp-Weeks.dta saved

.                 
.         *********************************************************
.         ********** Main analysis of 2.3.1 and 2.3.2 *************
.         *********************************************************
.                 xtset dirdyadid year
       panel variable:  dirdyadid (unbalanced)
        time variable:  year, 1945 to 2000, but with gaps
                delta:  1 unit

.                 global cvar1 = "democracy_2_lag cap_1_lag cap_2_lag initshare_lag de
> pendlow_lag majmaj_lag minmaj_lag majmin_lag contigdum_lag logdist_lag s_wt_glo_lag 
> s_lead_1_lag s_lead_2_lag time time2 time3"

.                 global cvar2 = "democracy_2_lag cap_1_lag cap_2_lag initshare_lag de
> pendlow_lag  s_wt_glo_lag s_lead_1_lag s_lead_2_lag time time2 time3 "

.                 * 2.3.1 logit *
.                 logit mzinit persrat_2014_1_lag milrat_2014_1_lag persxmil_lag $cvar
> 1 ///
>                         if democracy_1_lag==0 & newregime_1_lag!=1, robust cluster(d
> irdyadid)

Iteration 0:   log pseudolikelihood = -4510.2205  
Iteration 1:   log pseudolikelihood = -3953.9668  
Iteration 2:   log pseudolikelihood = -3893.5225  
Iteration 3:   log pseudolikelihood = -3411.2568  
Iteration 4:   log pseudolikelihood = -2921.1522  
Iteration 5:   log pseudolikelihood = -2889.4555  
Iteration 6:   log pseudolikelihood =  -2888.417  
Iteration 7:   log pseudolikelihood = -2888.4134  
Iteration 8:   log pseudolikelihood = -2888.4134  

Logistic regression                             Number of obs     =    268,458
                                                Wald chi2(19)     =    1849.76
                                                Prob > chi2       =     0.0000
Log pseudolikelihood = -2888.4134               Pseudo R2         =     0.3596

                               (Std. Err. adjusted for 12,885 clusters in dirdyadid)
------------------------------------------------------------------------------------
                   |               Robust
            mzinit |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------------+----------------------------------------------------------------
persrat_2014_1_lag |   1.075898   .2292225     4.69   0.000     .6266301    1.525166
 milrat_2014_1_lag |   1.255716    .275024     4.57   0.000     .7166791    1.794753
      persxmil_lag |  -.8091392   .3132224    -2.58   0.010    -1.423044   -.1952345
   democracy_2_lag |   .4747986   .1460084     3.25   0.001     .1886275    .7609697
         cap_1_lag |   12.72343    5.35424     2.38   0.017     2.229315    23.21755
         cap_2_lag |   3.739489   1.772205     2.11   0.035     .2660303    7.212948
     initshare_lag |  -.0372902   .2069213    -0.18   0.857    -.4428485    .3682681
     dependlow_lag |   16.05708   3.583929     4.48   0.000     9.032709    23.08145
        majmaj_lag |   .6129364   .8705086     0.70   0.481    -1.093229    2.319102
        minmaj_lag |   1.341257   .2765855     4.85   0.000      .799159    1.883354
        majmin_lag |  -.2299009   .7002981    -0.33   0.743     -1.60246    1.142658
     contigdum_lag |   2.232385   .6216942     3.59   0.000     1.013887    3.450884
       logdist_lag |  -.1567472   .0745908    -2.10   0.036    -.3029425   -.0105519
      s_wt_glo_lag |   -.039957   .3114834    -0.13   0.898    -.6504533    .5705393
      s_lead_1_lag |  -.4908675   .3674979    -1.34   0.182     -1.21115    .2294152
      s_lead_2_lag |   .9486852   .2630909     3.61   0.000     .4330366    1.464334
              time |  -33.13016   2.976932   -11.13   0.000    -38.96484   -27.29548
             time2 |   109.2502   17.00429     6.42   0.000     75.92245     142.578
             time3 |  -119.6375   27.32749    -4.38   0.000    -173.1984    -66.0766
             _cons |  -4.835141   .7233228    -6.68   0.000    -6.252828   -3.417455
------------------------------------------------------------------------------------

.                         est store weeks1

.                 * 2.3.1 logit Sample with xpers not missing *
.                 logit mzinit persrat_2014_1_lag milrat_2014_1_lag persxmil_lag $cvar
> 1 ///
>                         if democracy_1_lag==0 & newregime_1_lag!=1 & xpers~=. & pers
> rat_2014_1_lag~=.,  robust cluster(dirdyadid)

Iteration 0:   log pseudolikelihood = -4502.2676  
Iteration 1:   log pseudolikelihood = -3945.6087  
Iteration 2:   log pseudolikelihood =    -3869.6  
Iteration 3:   log pseudolikelihood = -3388.1368  
Iteration 4:   log pseudolikelihood = -2914.3906  
Iteration 5:   log pseudolikelihood = -2885.3028  
Iteration 6:   log pseudolikelihood =  -2884.399  
Iteration 7:   log pseudolikelihood =  -2884.396  
Iteration 8:   log pseudolikelihood =  -2884.396  

Logistic regression                             Number of obs     =    267,656
                                                Wald chi2(19)     =    1849.57
                                                Prob > chi2       =     0.0000
Log pseudolikelihood =  -2884.396               Pseudo R2         =     0.3593

                               (Std. Err. adjusted for 12,879 clusters in dirdyadid)
------------------------------------------------------------------------------------
                   |               Robust
            mzinit |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------------+----------------------------------------------------------------
persrat_2014_1_lag |   1.079063   .2290849     4.71   0.000     .6300654    1.528062
 milrat_2014_1_lag |   1.253283   .2750291     4.56   0.000     .7142359     1.79233
      persxmil_lag |  -.8062568   .3132965    -2.57   0.010    -1.420307   -.1922069
   democracy_2_lag |    .474204   .1459969     3.25   0.001     .1880554    .7603526
         cap_1_lag |   12.69842   5.360077     2.37   0.018     2.192865    23.20398
         cap_2_lag |   3.634722   1.786017     2.04   0.042      .134193    7.135251
     initshare_lag |  -.0375011   .2070387    -0.18   0.856    -.4432895    .3682873
     dependlow_lag |   16.08053   3.595182     4.47   0.000     9.034108    23.12696
        majmaj_lag |   .6191296   .8717252     0.71   0.478     -1.08942     2.32768
        minmaj_lag |   1.339615   .2778449     4.82   0.000     .7950493    1.884181
        majmin_lag |  -.2333591   .7008152    -0.33   0.739    -1.606932    1.140213
     contigdum_lag |   2.241924   .6213124     3.61   0.000     1.024174    3.459674
       logdist_lag |  -.1561303   .0745063    -2.10   0.036      -.30216   -.0101006
      s_wt_glo_lag |  -.0548906   .3115027    -0.18   0.860    -.6654247    .5556435
      s_lead_1_lag |  -.4928999    .367507    -1.34   0.180      -1.2132    .2274006
      s_lead_2_lag |   .9387194   .2634455     3.56   0.000     .4223757    1.455063
              time |  -33.09424   2.977779   -11.11   0.000    -38.93058    -27.2579
             time2 |   109.1885   17.00029     6.42   0.000     75.86849    142.5084
             time3 |  -119.5711   27.31547    -4.38   0.000    -173.1084   -66.03371
             _cons |   -4.83113   .7231008    -6.68   0.000    -6.248381   -3.413878
------------------------------------------------------------------------------------

.                         est store weeks2        

.                 * 2.3.1 logit Sample with xpers not missing & xpers instead of persr
> at *
.                 logit mzinit xpers milrat_2014_1_lag xpersx $cvar1 ///
>                         if democracy_1_lag==0 & newregime_1_lag!=1 & xpers~=. & pers
> rat_2014_1_lag~=.,  robust cluster(dirdyadid)

Iteration 0:   log pseudolikelihood = -4502.2676  
Iteration 1:   log pseudolikelihood = -3943.6988  
Iteration 2:   log pseudolikelihood = -3866.1497  
Iteration 3:   log pseudolikelihood = -3382.1636  
Iteration 4:   log pseudolikelihood = -2910.7924  
Iteration 5:   log pseudolikelihood =  -2882.429  
Iteration 6:   log pseudolikelihood = -2881.6521  
Iteration 7:   log pseudolikelihood = -2881.6502  
Iteration 8:   log pseudolikelihood = -2881.6502  

Logistic regression                             Number of obs     =    267,656
                                                Wald chi2(19)     =    1875.07
                                                Prob > chi2       =     0.0000
Log pseudolikelihood = -2881.6502               Pseudo R2         =     0.3600

                              (Std. Err. adjusted for 12,879 clusters in dirdyadid)
-----------------------------------------------------------------------------------
                  |               Robust
           mzinit |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
------------------+----------------------------------------------------------------
            xpers |   1.392194   .2545154     5.47   0.000     .8933532    1.891035
milrat_2014_1_lag |   1.194166   .2736562     4.36   0.000     .6578093    1.730522
    xpersxmil_lag |  -.9765609   .4178217    -2.34   0.019    -1.795476   -.1576455
  democracy_2_lag |   .5037554   .1486054     3.39   0.001     .2124942    .7950167
        cap_1_lag |   7.639287   5.344713     1.43   0.153    -2.836158    18.11473
        cap_2_lag |   3.824619   1.754594     2.18   0.029     .3856788     7.26356
    initshare_lag |  -.1152979   .2054354    -0.56   0.575     -.517944    .2873481
    dependlow_lag |   13.88261   4.237839     3.28   0.001       5.5766    22.18862
       majmaj_lag |   1.412574   .8858144     1.59   0.111    -.3235898    3.148739
       minmaj_lag |   1.307913   .2715111     4.82   0.000     .7757611    1.840065
       majmin_lag |   .5997121   .6917097     0.87   0.386    -.7560139    1.955438
    contigdum_lag |   2.266607   .6008498     3.77   0.000     1.088963    3.444251
      logdist_lag |   -.151675   .0719434    -2.11   0.035    -.2926815   -.0106684
     s_wt_glo_lag |  -.0000609   .3047536    -0.00   1.000    -.5973671    .5972452
     s_lead_1_lag |  -.4616134   .3606196    -1.28   0.201    -1.168415    .2451881
     s_lead_2_lag |   .9406329   .2596188     3.62   0.000     .4317893    1.449476
             time |  -32.34743   2.967889   -10.90   0.000    -38.16438   -26.53047
            time2 |    104.248   16.93463     6.16   0.000     71.05677    137.4393
            time3 |  -112.6239   27.21904    -4.14   0.000    -165.9723    -59.2756
            _cons |  -4.881334   .7106337    -6.87   0.000     -6.27415   -3.488517
-----------------------------------------------------------------------------------

.                         est store weeks3        

.                 * 2.3.2 FE logit *
.                 xtlogit mzinit persrat_2014_1_lag milrat_2014_1_lag persxmil_lag dem
> ocracy_2_lag ///
>                         cap_1_lag cap_2_lag initshare_lag dependlow_lag  s_wt_glo_la
> g s_lead_1_lag s_lead_2_lag ///
>                         time time2 time3 if democracy_1_lag==0 & newregime_1_lag!=1,
>  fe
note: multiple positive outcomes within groups encountered.
note: 12,620 groups (260,280 obs) dropped because of all positive or
      all negative outcomes.

Iteration 0:   log likelihood = -1660.5624  
Iteration 1:   log likelihood = -1606.7278  
Iteration 2:   log likelihood = -1606.4124  
Iteration 3:   log likelihood = -1606.4124  

Conditional fixed-effects logistic regression   Number of obs     =      8,178
Group variable: dirdyadid                       Number of groups  =        265

                                                Obs per group:
                                                              min =          4
                                                              avg =       30.9
                                                              max =         49

                                                LR chi2(14)       =     106.88
Log likelihood  = -1606.4124                    Prob > chi2       =     0.0000

------------------------------------------------------------------------------------
            mzinit |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------------+----------------------------------------------------------------
persrat_2014_1_lag |   1.115854   .3331729     3.35   0.001     .4628473    1.768861
 milrat_2014_1_lag |   1.484225   .5259581     2.82   0.005     .4533664    2.515084
      persxmil_lag |  -1.292895   .6143963    -2.10   0.035     -2.49709   -.0887009
   democracy_2_lag |  -.1555948   .2395318    -0.65   0.516    -.6250684    .3138789
         cap_1_lag |   1.552688   10.68098     0.15   0.884    -19.38165    22.48702
         cap_2_lag |   9.169565   2.913733     3.15   0.002     3.458754    14.88038
     initshare_lag |   .2625375   1.053234     0.25   0.803    -1.801764    2.326839
     dependlow_lag |   27.71921   17.16413     1.61   0.106    -5.921871    61.36029
      s_wt_glo_lag |   .0016111   .5069043     0.00   0.997    -.9919031    .9951253
      s_lead_1_lag |  -1.741743   .5762451    -3.02   0.003    -2.871163   -.6123233
      s_lead_2_lag |   .3467419   .5681709     0.61   0.542    -.7668526    1.460336
              time |     -8.097   3.099271    -2.61   0.009    -14.17146    -2.02254
             time2 |   18.74344   19.97738     0.94   0.348    -20.41151    57.89838
             time3 |     29.738   34.17101     0.87   0.384    -37.23596    96.71195
------------------------------------------------------------------------------------

.                         est store weeks4        

.                 * 2.3.2 FE logit reduced sample with xpers not missing *
.                 xtlogit mzinit persrat_2014_1_lag milrat_2014_1_lag persxmil_lag $cv
> ar2 ///
>                         if democracy_1_lag==0 & newregime_1_lag!=1 & xpers~=. & pers
> rat_2014_1_lag~=., fe
note: multiple positive outcomes within groups encountered.
note: 12,615 groups (259,498 obs) dropped because of all positive or
      all negative outcomes.

Iteration 0:   log likelihood = -1657.4334  
Iteration 1:   log likelihood = -1603.4497  
Iteration 2:   log likelihood = -1603.1301  
Iteration 3:   log likelihood = -1603.1301  

Conditional fixed-effects logistic regression   Number of obs     =      8,158
Group variable: dirdyadid                       Number of groups  =        264

                                                Obs per group:
                                                              min =          4
                                                              avg =       30.9
                                                              max =         49

                                                LR chi2(14)       =     107.07
Log likelihood  = -1603.1301                    Prob > chi2       =     0.0000

------------------------------------------------------------------------------------
            mzinit |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------------+----------------------------------------------------------------
persrat_2014_1_lag |   1.150027   .3333925     3.45   0.001     .4965893    1.803464
 milrat_2014_1_lag |   1.565617   .5290963     2.96   0.003     .5286075    2.602627
      persxmil_lag |   -1.35991   .6155831    -2.21   0.027     -2.56643   -.1533889
   democracy_2_lag |  -.1549387   .2393066    -0.65   0.517     -.623971    .3140936
         cap_1_lag |   1.739554    10.6866     0.16   0.871     -19.2058    22.68491
         cap_2_lag |   8.957911   2.924238     3.06   0.002     3.226509    14.68931
     initshare_lag |   .2581114   1.053173     0.25   0.806     -1.80607    2.322293
     dependlow_lag |   27.75139   17.21757     1.61   0.107    -5.994438    61.49721
      s_wt_glo_lag |   .0367004    .507219     0.07   0.942    -.9574307    1.030831
      s_lead_1_lag |  -1.767366   .5766393    -3.06   0.002    -2.897559   -.6371743
      s_lead_2_lag |   .3915696   .5685509     0.69   0.491    -.7227697    1.505909
              time |  -8.084222   3.100414    -2.61   0.009    -14.16092   -2.007521
             time2 |   18.72036   19.97997     0.94   0.349    -20.43967    57.88038
             time3 |   29.83046   34.17304     0.87   0.383    -37.14748    96.80839
------------------------------------------------------------------------------------

.                         est store weeks5

.                         gen sampleA = e(sample)==1      

.                 * 2.3.2 FE logit reduced sample with xpers not missing & xpers inste
> ad of persrat *
.                 xtlogit mzinit xpers milrat_2014_1_lag xpersx $cvar2 if democracy_1_
> lag==0 & ///
>                         newregime_1_lag!=1 & xpers~=. & persrat_2014_1_lag~=., fe
note: multiple positive outcomes within groups encountered.
note: 12,615 groups (259,498 obs) dropped because of all positive or
      all negative outcomes.

Iteration 0:   log likelihood = -1660.2744  
Iteration 1:   log likelihood = -1608.0297  
Iteration 2:   log likelihood = -1607.7313  
Iteration 3:   log likelihood = -1607.7313  

Conditional fixed-effects logistic regression   Number of obs     =      8,158
Group variable: dirdyadid                       Number of groups  =        264

                                                Obs per group:
                                                              min =          4
                                                              avg =       30.9
                                                              max =         49

                                                LR chi2(14)       =      97.87
Log likelihood  = -1607.7313                    Prob > chi2       =     0.0000

-----------------------------------------------------------------------------------
           mzinit |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
------------------+----------------------------------------------------------------
            xpers |   .3887555   .3355217     1.16   0.247     -.268855    1.046366
milrat_2014_1_lag |   .6544375    .466575     1.40   0.161    -.2600326    1.568908
    xpersxmil_lag |  -.1028453   .6483967    -0.16   0.874     -1.37368    1.167989
  democracy_2_lag |  -.1645793   .2384587    -0.69   0.490    -.6319498    .3027912
        cap_1_lag |  -5.352864   10.24839    -0.52   0.601    -25.43934    14.73361
        cap_2_lag |   9.375053   2.875939     3.26   0.001     3.738316    15.01179
    initshare_lag |   .3741399   1.056835     0.35   0.723    -1.697219    2.445499
    dependlow_lag |   26.67775   16.32733     1.63   0.102    -5.323223    58.67872
     s_wt_glo_lag |  -.1672913   .5024775    -0.33   0.739    -1.152129    .8175466
     s_lead_1_lag |  -1.511541   .5661626    -2.67   0.008    -2.621199   -.4018824
     s_lead_2_lag |   .0329554   .5576539     0.06   0.953    -1.060026    1.125937
             time |  -7.693298   3.088699    -2.49   0.013    -13.74704   -1.639559
            time2 |   13.45609   19.83976     0.68   0.498    -25.42913     52.3413
            time3 |   39.56376   33.93445     1.17   0.244    -26.94653    106.0741
-----------------------------------------------------------------------------------

.                         est store weeks6

.                   label var xpers  "G-pers"

.                   label var milrat_2014_1_lag  "W-mil"

.                   label var persrat_2014_1_lag  "W-pers"

.                   label var persxmil_lag  `""W-pers" "x    " "W-mil ""'

.                   label var xpersx  `""G-pers" "x    " "W-mil ""'                

.                         
.                 estout weeks1 weeks2 weeks3 weeks4 weeks5 weeks6 using TableD1.tex, 
> cells(b(star  fmt(%9.3f)) se(par fmt(%9.2f))) ///
>                 stats(r2 N N_clust) style(tex) replace label starlevels(* 0.05) titl
> e(\label{tabB1})    
(note: file TableD1.tex not found)
(output written to TableD1.tex)

.          
.                 * Figure for text *
.                   label var xpers  "G-pers"

.                   label var milrat_2014_1_lag  "W-mil"

.                   label var persrat_2014_1_lag  "W-pers"

.                   label var persxmil_lag  `""W-pers" "x    " "W-mil ""'

.                   label var xpersx  `""G-pers" "x    " "W-mil ""'

.                 coefplot (weeks1, msymbol(d)) (weeks2, msymbol(t)) (weeks3, msymbol(
> s)), title("Logit", size(medium)) ///
>                         scheme(plottig) drop(_cons democracy_2_lag  cap_1_lag cap_2_
> lag initshare_lag dependlow_lag majmaj_lag ///
>                          minmaj_lag majmin_lag contigdum_lag logdist_lag s_wt_glo_la
> g s_lead_1_lag s_lead_2_lag time time2 time3) xline(0) ///
>                         grid(glcolor(gs15)) mfcolor(white) xlabel(-2.0 (.5) 2.0)  le
> vels(95 90) xtitle("Coefficient estimate", height(6)) ///
>                         legend(lab(3 "Original") lab(6 "Orignal-adjusted sample") la
> b(9 "G-pers-adjusted sample") size(vsmall) pos(6) ring(1.5) col(3)) ///
>                         ysize(1) xsize(1.5) saving(r1, replace)                 
(note: file r1.gph not found)
(file r1.gph saved)

.                 coefplot (weeks4, msymbol(d)) (weeks5, msymbol(t)) (weeks6, msymbol(
> s)) , title("FE-Logit", size(medium)) ///
>                         scheme(plottig) drop(_cons democracy_2_lag  cap_1_lag cap_2_
> lag initshare_lag dependlow_lag majmaj_lag ///
>                          minmaj_lag majmin_lag contigdum_lag logdist_lag s_wt_glo_la
> g s_lead_1_lag s_lead_2_lag time time2 time3) xline(0) ///
>                         grid(glcolor(gs15)) mfcolor(white) xlabel(-2.0 (.5) 2.0)  le
> vels(95 90) xtitle("Coefficient estimate", height(6)) ///
>                         legend(lab(3 "Original") lab(6 "Orignal-adjusted sample") la
> b(9 "G-pers-adjusted sample") size(vsmall) pos(6) ring(1.5) col(3)) ///
>                         ysize(1) xsize(1.5) saving(r2, replace)         
(note: file r2.gph not found)
(file r2.gph saved)

.                 gr combine r1.gph r2.gph, iscale(.75) xsize(6)  

.                 graph export "$dir/golden/MID-Main.pdf", as(pdf) replace
(file /Users/lee/Dropbox/Datavers/golden/MID-Main.pdf written in PDF format)

.                                          
.                 est restore weeks4
(results weeks4 are active now)

.                 tab milrat_2014_1_lag if e(sample)

      W-mil |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |      2,398       29.32       29.32
         .2 |        396        4.84       34.16
        .25 |      1,535       18.77       52.93
   .3333333 |        805        9.84       62.78
         .4 |        116        1.42       64.20
         .5 |         66        0.81       65.00
         .6 |        818       10.00       75.01
   .6666667 |        208        2.54       77.55
        .75 |        192        2.35       79.90
         .8 |        846       10.34       90.24
          1 |        798        9.76      100.00
------------+-----------------------------------
      Total |      8,178      100.00

.                 margins, dydx(persrat_2014_1_lag) at(milrat_2014_1_lag=(0(.05)1)) vs
> quish post

Average marginal effects                        Number of obs     =      8,178
Model VCE    : OIM

Expression   : Pr(mzinit|fixed effect is 0), predict(pu0)
dy/dx w.r.t. : persrat_2014_1_lag
1._at        : milrat_201~g    =           0
2._at        : milrat_201~g    =         .05
3._at        : milrat_201~g    =          .1
4._at        : milrat_201~g    =         .15
5._at        : milrat_201~g    =          .2
6._at        : milrat_201~g    =         .25
7._at        : milrat_201~g    =          .3
8._at        : milrat_201~g    =         .35
9._at        : milrat_201~g    =          .4
10._at       : milrat_201~g    =         .45
11._at       : milrat_201~g    =          .5
12._at       : milrat_201~g    =         .55
13._at       : milrat_201~g    =          .6
14._at       : milrat_201~g    =         .65
15._at       : milrat_201~g    =          .7
16._at       : milrat_201~g    =         .75
17._at       : milrat_201~g    =          .8
18._at       : milrat_201~g    =         .85
19._at       : milrat_201~g    =          .9
20._at       : milrat_201~g    =         .95
21._at       : milrat_201~g    =           1

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
persrat_20~g |
         _at |
          1  |   .2299356   .0697715     3.30   0.001      .093186    .3666851
          2  |   .2269685   .0698426     3.25   0.001     .0900794    .3638575
          3  |    .223619   .0698912     3.20   0.001     .0866346    .3606033
          4  |   .2199069   .0699189     3.15   0.002     .0828684    .3569454
          5  |   .2158542   .0699283     3.09   0.002     .0787973    .3529111
          6  |   .2114846   .0699226     3.02   0.002     .0744387    .3485305
          7  |   .2068234   .0699051     2.96   0.003     .0698119    .3438348
          8  |   .2018972    .069878     2.89   0.004     .0649388    .3388556
          9  |   .1967337   .0698427     2.82   0.005     .0598444    .3336229
         10  |   .1913612   .0697991     2.74   0.006     .0545574     .328165
         11  |   .1858085   .0697454     2.66   0.008     .0491102    .3225069
         12  |   .1801046   .0696779     2.58   0.010     .0435384    .3166708
         13  |   .1742781   .0695916     2.50   0.012      .037881    .3106752
         14  |   .1683575   .0694801     2.42   0.015     .0321791    .3045359
         15  |   .1623704   .0693355     2.34   0.019     .0264752    .2982656
         16  |   .1563436   .0691498     2.26   0.024     .0208125    .2918746
         17  |   .1503027   .0689141     2.18   0.029     .0152335    .2853719
         18  |   .1442722     .06862     2.10   0.036     .0097795     .278765
         19  |   .1382751   .0682594     2.03   0.043     .0044891    .2720611
         20  |   .1323327    .067825     1.95   0.051    -.0006019    .2652672
         21  |   .1264647   .0673106     1.88   0.060    -.0054616     .258391
------------------------------------------------------------------------------

.                 matrix at=e(at)

.                 matrix at=at[1...,"milrat_2014_1_lag"]

.                 matrix list at

at[21,1]
        milrat_201~g
 1._at             0
 2._at           .05
 3._at            .1
 4._at           .15
 5._at            .2
 6._at           .25
 7._at            .3
 8._at           .35
 9._at            .4
10._at           .45
11._at            .5
12._at           .55
13._at            .6
14._at           .65
15._at            .7
16._at           .75
17._at            .8
18._at           .85
19._at            .9
20._at           .95
21._at             1

.                 parmest, norestore
command parmest is unrecognized
r(199);

end of do-file

r(199);

. ssc install parmest
checking parmest consistency and verifying not already installed...
installing into /Users/lee/Library/Application Support/Stata/ado/plus/...
installation complete.

. do "/var/folders/mg/c8zmtcx92mgb39c8hh3hcw8h0000gn/T//SD00697.000000"

.                 est restore weeks4
(results weeks4 are active now)

.                 tab milrat_2014_1_lag if e(sample)

      W-mil |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |      2,398       29.32       29.32
         .2 |        396        4.84       34.16
        .25 |      1,535       18.77       52.93
   .3333333 |        805        9.84       62.78
         .4 |        116        1.42       64.20
         .5 |         66        0.81       65.00
         .6 |        818       10.00       75.01
   .6666667 |        208        2.54       77.55
        .75 |        192        2.35       79.90
         .8 |        846       10.34       90.24
          1 |        798        9.76      100.00
------------+-----------------------------------
      Total |      8,178      100.00

.                 margins, dydx(persrat_2014_1_lag) at(milrat_2014_1_lag=(0(.05)1)) vs
> quish post

Average marginal effects                        Number of obs     =      8,178
Model VCE    : OIM

Expression   : Pr(mzinit|fixed effect is 0), predict(pu0)
dy/dx w.r.t. : persrat_2014_1_lag
1._at        : milrat_201~g    =           0
2._at        : milrat_201~g    =         .05
3._at        : milrat_201~g    =          .1
4._at        : milrat_201~g    =         .15
5._at        : milrat_201~g    =          .2
6._at        : milrat_201~g    =         .25
7._at        : milrat_201~g    =          .3
8._at        : milrat_201~g    =         .35
9._at        : milrat_201~g    =          .4
10._at       : milrat_201~g    =         .45
11._at       : milrat_201~g    =          .5
12._at       : milrat_201~g    =         .55
13._at       : milrat_201~g    =          .6
14._at       : milrat_201~g    =         .65
15._at       : milrat_201~g    =          .7
16._at       : milrat_201~g    =         .75
17._at       : milrat_201~g    =          .8
18._at       : milrat_201~g    =         .85
19._at       : milrat_201~g    =          .9
20._at       : milrat_201~g    =         .95
21._at       : milrat_201~g    =           1

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
persrat_20~g |
         _at |
          1  |   .2299356   .0697715     3.30   0.001      .093186    .3666851
          2  |   .2269685   .0698426     3.25   0.001     .0900794    .3638575
          3  |    .223619   .0698912     3.20   0.001     .0866346    .3606033
          4  |   .2199069   .0699189     3.15   0.002     .0828684    .3569454
          5  |   .2158542   .0699283     3.09   0.002     .0787973    .3529111
          6  |   .2114846   .0699226     3.02   0.002     .0744387    .3485305
          7  |   .2068234   .0699051     2.96   0.003     .0698119    .3438348
          8  |   .2018972    .069878     2.89   0.004     .0649388    .3388556
          9  |   .1967337   .0698427     2.82   0.005     .0598444    .3336229
         10  |   .1913612   .0697991     2.74   0.006     .0545574     .328165
         11  |   .1858085   .0697454     2.66   0.008     .0491102    .3225069
         12  |   .1801046   .0696779     2.58   0.010     .0435384    .3166708
         13  |   .1742781   .0695916     2.50   0.012      .037881    .3106752
         14  |   .1683575   .0694801     2.42   0.015     .0321791    .3045359
         15  |   .1623704   .0693355     2.34   0.019     .0264752    .2982656
         16  |   .1563436   .0691498     2.26   0.024     .0208125    .2918746
         17  |   .1503027   .0689141     2.18   0.029     .0152335    .2853719
         18  |   .1442722     .06862     2.10   0.036     .0097795     .278765
         19  |   .1382751   .0682594     2.03   0.043     .0044891    .2720611
         20  |   .1323327    .067825     1.95   0.051    -.0006019    .2652672
         21  |   .1264647   .0673106     1.88   0.060    -.0054616     .258391
------------------------------------------------------------------------------

.                 matrix at=e(at)

.                 matrix at=at[1...,"milrat_2014_1_lag"]

.                 matrix list at

at[21,1]
        milrat_201~g
 1._at             0
 2._at           .05
 3._at            .1
 4._at           .15
 5._at            .2
 6._at           .25
 7._at            .3
 8._at           .35
 9._at            .4
10._at           .45
11._at            .5
12._at           .55
13._at            .6
14._at           .65
15._at            .7
16._at           .75
17._at            .8
18._at           .85
19._at            .9
20._at           .95
21._at             1

.                 parmest, norestore

.                 svmat at

.                 twoway (line min95 at1, lpattern(dash) lcol(blue)) (line estimate at
> 1,lcol(blue)) ///
>                         (line max95 at1,lcol(blue) lpattern(dash)), legend(order (1 
> "Upper 95% c.i." 3 "Lower 95% c.i.")) ///
>                         yline(0,lcol(gs5))  xtitle(Score on militarism index) title(
> W-personalism) legend(pos(6) col(2)) ///
>                         ytitle(Marginal effect of personalism) saving(r1.gph,replace
> ) scheme(plottig)ylab(-.1(.1).4)
(file r1.gph saved)

.                 use "$dir/temp-Weeks.dta",clear 

.                 qui xtlogit mzinit xpers milrat_2014_1_lag xpersx $cvar2 if democrac
> y_1_lag==0 & ///
>                         newregime_1_lag!=1 & xpers~=. & persrat_2014_1_lag~=., fe

.                 est store weeks6

.                 est restore weeks6
(results weeks6 are active now)

.                 tab milrat_2014_1_lag if e(sample)

milrat_2014 |
     _1_lag |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |      2,396       29.37       29.37
         .2 |        396        4.85       34.22
        .25 |      1,535       18.82       53.04
   .3333333 |        801        9.82       62.86
         .4 |        116        1.42       64.28
         .5 |         66        0.81       65.09
         .6 |        816       10.00       75.09
   .6666667 |        208        2.55       77.64
        .75 |        186        2.28       79.92
         .8 |        846       10.37       90.29
          1 |        792        9.71      100.00
------------+-----------------------------------
      Total |      8,158      100.00

.                 margins, dydx(xpers) at(milrat_2014_1_lag=(0(.05)1)) vsquish post

Average marginal effects                        Number of obs     =      8,158
Model VCE    : OIM

Expression   : Pr(mzinit|fixed effect is 0), predict(pu0)
dy/dx w.r.t. : xpers
1._at        : milrat_201~g    =           0
2._at        : milrat_201~g    =         .05
3._at        : milrat_201~g    =          .1
4._at        : milrat_201~g    =         .15
5._at        : milrat_201~g    =          .2
6._at        : milrat_201~g    =         .25
7._at        : milrat_201~g    =          .3
8._at        : milrat_201~g    =         .35
9._at        : milrat_201~g    =          .4
10._at       : milrat_201~g    =         .45
11._at       : milrat_201~g    =          .5
12._at       : milrat_201~g    =         .55
13._at       : milrat_201~g    =          .6
14._at       : milrat_201~g    =         .65
15._at       : milrat_201~g    =          .7
16._at       : milrat_201~g    =         .75
17._at       : milrat_201~g    =          .8
18._at       : milrat_201~g    =         .85
19._at       : milrat_201~g    =          .9
20._at       : milrat_201~g    =         .95
21._at       : milrat_201~g    =           1

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
xpers        |
         _at |
          1  |   .0874042   .0751188     1.16   0.245    -.0598259    .2346342
          2  |   .0874816   .0751296     1.16   0.244    -.0597697    .2347328
          3  |   .0875223   .0750897     1.17   0.244    -.0596507    .2346954
          4  |   .0875263   .0749992     1.17   0.243    -.0594695     .234522
          5  |   .0874932   .0748586     1.17   0.242    -.0592269    .2342133
          6  |   .0874231   .0746683     1.17   0.242    -.0589241    .2337703
          7  |    .087316   .0744293     1.17   0.241    -.0585627    .2331947
          8  |   .0871719   .0741424     1.18   0.240    -.0581445    .2324883
          9  |   .0869909   .0738089     1.18   0.239    -.0576718    .2316536
         10  |   .0867732   .0734301     1.18   0.237    -.0571472    .2306936
         11  |   .0865191   .0730077     1.19   0.236    -.0565735    .2296116
         12  |   .0862288   .0725435     1.19   0.235    -.0559539    .2284114
         13  |   .0859027   .0720394     1.19   0.233    -.0552919    .2270972
         14  |   .0855412   .0714975     1.20   0.232    -.0545914    .2256737
         15  |   .0851448   .0709202     1.20   0.230    -.0538562    .2241458
         16  |    .084714   .0703099     1.20   0.228    -.0530909     .222519
         17  |   .0842495   .0696693     1.21   0.227    -.0522998    .2207988
         18  |   .0837518    .069001     1.21   0.225    -.0514876    .2189913
         19  |   .0832217   .0683079     1.22   0.223    -.0506593    .2171028
         20  |   .0826599    .067593     1.22   0.221      -.04982    .2151398
         21  |   .0820672   .0668593     1.23   0.220    -.0489746     .213109
------------------------------------------------------------------------------

.                 matrix at=e(at)

.                 matrix at=at[1...,"milrat_2014_1_lag"]

.                 matrix list at

at[21,1]
        milrat_201~g
 1._at             0
 2._at           .05
 3._at            .1
 4._at           .15
 5._at            .2
 6._at           .25
 7._at            .3
 8._at           .35
 9._at            .4
10._at           .45
11._at            .5
12._at           .55
13._at            .6
14._at           .65
15._at            .7
16._at           .75
17._at            .8
18._at           .85
19._at            .9
20._at           .95
21._at             1

.                 parmest, norestore

.                 svmat at

.                 twoway (line min95 at1, lpattern(dash) lcol(blue)) (line estimate at
> 1,lcol(blue)) ///
>                         (line max95 at1,lcol(blue) lpattern(dash)), legend(order (1 
> "Upper 95% c.i." 3 "Lower 95% c.i.")) ///
>                         yline(0,lcol(gs5))  xtitle(Score on militarism index) title(
> G-personalism) legend(pos(6) col(2)) ///
>                         ytitle(Marginal effect of personalism) saving(r2.gph,replace
> ) scheme(plottig) ylab(-.1(.1).4)
(file r2.gph saved)

.                 gr combine r1.gph r2.gph, iscale(.8) xsize(6)  

.                 graph export "$dir/golden/MID-Margins.pdf", as(pdf) replace
(file /Users/lee/Dropbox/Datavers/golden/MID-Margins.pdf written in PDF format)

. 
.                 
.         ************************************************
.         ********** Analysis for discussion *************
.         ************************************************        
.                 use "$dir/temp-Weeks.dta",clear 

.                 qui xtlogit mzinit persrat_2014_1_lag milrat_2014_1_lag persxmil_lag
>  $cvar2 ///
>                         if democracy_1_lag==0 & newregime_1_lag!=1 & xpers~=. & pers
> rat_2014_1_lag~=., fe

.                 gen sampleA = e(sample)==1

.                 * Baseline MID initiation rate, by country (in sample) *        
.                         gen cnum =.
(1,079,606 missing values generated)

.                         gen counter=_n

.                         gen csum =.
(1,079,606 missing values generated)

.                         local i=1

.                         levelsof cow if sampleA==1, local(levels) 
40 41 42 70 90 92 93 95 130 135 140 145 150 155 160 230 235 265 290 310 315 339 345 35
> 0 355 365 432 433 435 438 450 452 471 475 483 484 490 500 501 510 520 530 540 552 61
> 5 616 620 625 645 651 652 710 713 731 732 770 775 800 812 816 820 840 850

.                         foreach l of local levels {
  2.                                 qui sum mzinit if cow == `l' & sampleA==1
  3.                                 qui replace csum = r(mean) if counter==`i'
  4.                                 qui replace cnum =`l' if counter==`i'
  5.                                 local i =`i' +1
  6.                         }

.                         hist csum if cnum~=., bin(50) xlab(0(.1).6) xtitle(Country b
> aseline probability of MID initiation) freq
(bin=50, start=.02631579, width=.01147368)

.                         
.                         * China detail *
.                         gen mao_lo = cow==710 & year<1967

.                         gen mao_hi = cow==710 & year>=1967 & year<=1976

.                         gen mao_not = cow==710 & year>1976

.                         sum csum if cnum==710

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
        csum |          1    .0908059           .   .0908059   .0908059

.                         tab mzinit if cow==710 & sampleA==1

     mzinit |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |        801       90.92       90.92
          1 |         80        9.08      100.00
------------+-----------------------------------
      Total |        881      100.00

.                         tab mzinit if mao_lo==1 & sampleA==1

     mzinit |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |        217       83.46       83.46
          1 |         43       16.54      100.00
------------+-----------------------------------
      Total |        260      100.00

.                         tab mzinit if mao_hi==1 & sampleA==1

     mzinit |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |        177       93.65       93.65
          1 |         12        6.35      100.00
------------+-----------------------------------
      Total |        189      100.00

.                         tab mzinit if mao_not==1 & sampleA==1

     mzinit |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |        407       94.21       94.21
          1 |         25        5.79      100.00
------------+-----------------------------------
      Total |        432      100.00

.                         
.                         * Libya detail *
.                         gen gaddafi_lo = cow==620 & year<1976 

.                         gen gaddafi_hi = cow==620 & year>=1976 

.                         sum csum if cnum==620

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
        csum |          1    .0973451           .   .0973451   .0973451

.                         tab mzinit if cow==620 & sampleA==1

     mzinit |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |        306       90.27       90.27
          1 |         33        9.73      100.00
------------+-----------------------------------
      Total |        339      100.00

.                         tab mzinit if gaddafi_lo==1 & sampleA==1

     mzinit |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |         61       95.31       95.31
          1 |          3        4.69      100.00
------------+-----------------------------------
      Total |         64      100.00

.                         tab mzinit if gaddafi_hi==1 & sampleA==1

     mzinit |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |        245       89.09       89.09
          1 |         30       10.91      100.00
------------+-----------------------------------
      Total |        275      100.00

.                         
.                         * North Korea detail *
.                         gen kim_lo = cow==731 & year<1959

.                         gen kim_hi = cow==731 & year>=1959 & year<=1994 

.                         gen kim_not = cow==731 & year>1994 

.                         sum csum if cnum==731

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
        csum |          1    .1557377           .   .1557377   .1557377

.                         tab mzinit if cow==731 & sampleA==1

     mzinit |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |        206       84.43       84.43
          1 |         38       15.57      100.00
------------+-----------------------------------
      Total |        244      100.00

.                         tab mzinit if kim_lo==1 & sampleA==1

     mzinit |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |         29       85.29       85.29
          1 |          5       14.71      100.00
------------+-----------------------------------
      Total |         34      100.00

.                         tab mzinit if kim_hi==1 & sampleA==1              

     mzinit |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |        154       85.56       85.56
          1 |         26       14.44      100.00
------------+-----------------------------------
      Total |        180      100.00

.                         tab mzinit if kim_not==1 & sampleA==1   

     mzinit |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |         23       76.67       76.67
          1 |          7       23.33      100.00
------------+-----------------------------------
      Total |         30      100.00

.                         
.                         * China *
.                         egen yrMID710 = mean(mzinit) if cow==710 & sampleA==1,by(yea
> r)
(1078725 missing values generated)

.                         tssmooth ma MAyrMID710=yrMID710,window(1 1 1)
The smoother applied was
     by dirdyadid : (1/3)*[x(t-1) + 1*x(t) + x(t+1)]; x(t)= yrMID710

.                         egen tagyrMID710 =tag(year) if yrMID710~=.

.                         label var year "Year"

.                         twoway (bar mao_lo year if tagyrMID710==1,col(gs14)) /// 
>                                 (bar mao_hi year if tagyrMID710==1,col(gs12)) ///
>                                 (line xpers year if tagyrMID710==1,lcol(blue) lpat(s
> olid)) ///
>                                 (line MAyrMID710 year if tagyrMID710==1,lcol(gs2)lpa
> t(solid) yaxis(2) ytitle(MID rate, axis(2))  ///
>                                 legend(lab(1 "Mao pre-Cultural Revolution") lab(2 "M
> ao 1967-1976")lab(3 "G-pers") lab(4 "MID rate") ///
>                                 pos(6) col(2))saving(r1,replace)title(G-personalism)
> )
(file r1.gph saved)

.                         twoway (bar mao_lo year if tagyrMID710==1,col(gs14)) /// 
>                                 (bar mao_hi year if tagyrMID710==1,col(gs12)) ///
>                                 (line persrat_2014_1_lag year if tagyrMID710==1,lcol
> (red)lpat(solid)) ///
>                                 (line MAyrMID710 year if tagyrMID710==1,lcol(gs2)lpa
> t(solid) yaxis(2) ytitle(MID rate, axis(2))  ///
>                                 legend(lab(1 "Mao pre-Cultural Revolution") lab(2 "M
> ao 1967-1976")lab(3 "W-pers") lab(4 "MID rate") ///
>                                 pos(6) col(2))saving(r2,replace)title(W-personalism)
> )
(file r2.gph saved)

.                         gr combine r1.gph r2.gph, xsize(8)

.                         drop yrMID710 tagyrMID710 MAyrMID710

.                         graph export "$dir/golden/MID-China.pdf", as(pdf) replace
(file /Users/lee/Dropbox/Datavers/golden/MID-China.pdf written in PDF format)

. 
.                         * Libya *
.                         egen yrMID620 = mean(mzinit) if cow==620 & sampleA==1,by(yea
> r)
(1079267 missing values generated)

.                         tssmooth ma MAyrMID620=yrMID620,window(1 1 1)
The smoother applied was
     by dirdyadid : (1/3)*[x(t-1) + 1*x(t) + x(t+1)]; x(t)= yrMID620

.                         egen tagyrMID620=tag(year) if yrMID620~=.

.                         label var year "Year"

.                         twoway (bar gaddafi_lo year if tagyrMID620==1,col(gs14)) ///
>  
>                                 (bar gaddafi_hi year if tagyrMID620==1,col(gs12)) //
> /
>                                 (line xpers year if tagyrMID620==1,lcol(blue) lpat(s
> olid)) ///
>                                 (line MAyrMID620 year if tagyrMID620==1,lcol(gs2)lpa
> t(solid) yaxis(2) ytitle(MID rate, axis(2))  ///
>                                 legend(lab(1 "Gaddafi 1969-1975") lab(2 "Gaddafi 197
> 6-2010") lab(3 "G-pers") lab(4 "MID rate") ///
>                                 pos(6) col(2))saving(r1,replace)title(G-personalism)
> )
(file r1.gph saved)

.                         twoway (bar gaddafi_lo year if tagyrMID620==1,col(gs14)) ///
>  
>                                 (bar gaddafi_hi year if tagyrMID620==1,col(gs12)) //
> /
>                                 (line persrat_2014_1_lag year if tagyrMID620==1,lcol
> (red)lpat(solid)) ///
>                                 (line MAyrMID620 year if tagyrMID620==1,lcol(gs2)lpa
> t(solid) yaxis(2) ytitle(MID rate, axis(2))  ///
>                                 legend(lab(1 "Gaddafi 1969-1975") lab(2 "Gaddafi 197
> 6-2010")lab(3 "W-pers") lab(4 "MID rate") ///
>                                 pos(6) col(2))saving(r2,replace)title(W-personalism)
> )
(file r2.gph saved)

.                         gr combine r1.gph r2.gph, xsize(8)

.                         drop tagyrMID620 tagyrMID620 tagyrMID620

.                         graph export "$dir/golden/MID-Libya.pdf", as(pdf) replace
(file /Users/lee/Dropbox/Datavers/golden/MID-Libya.pdf written in PDF format)

.                  
.         *****************************************************************
.         ********** Weeks Figure Appendix A: 2.3.3 and 2.3.4 *************
.         *****************************************************************
.                         use "$dir/temp-Weeks.dta",clear 

.                         xtset dirdyadid year
       panel variable:  dirdyadid (unbalanced)
        time variable:  year, 1945 to 2000, but with gaps
                delta:  1 unit

.                         gen persrat_2014_1_laga=persrat_2014_1_lag
(709,962 missing values generated)

.                         replace persrat_2014_1_laga=0 if democracy_1_lag==1
(315,908 real changes made)

.                         gen persxmil_laga=persrat_2014_1_laga*milrat_2014_1_lag
(415,634 missing values generated)

.                         gen xpers0dem = xpers
(517,195 missing values generated)

.                         recode xpers0dem (.=0) if  democracy_1_lag==1
(xpers0dem: 299352 changes made)

.                         gen xpers0demxmil = xpers0dem*milrat_2014_1_lag
(375,279 missing values generated)

.                         *2.3.3
.                         logit mzinit persrat_2014_1_laga milrat_2014_1_lag persxmil_
> laga democracy_2_lag cap_1_lag cap_2_lag initshare_lag dependlow_lag majmaj_lag minm
> aj_lag majmin_lag contigdum_lag logdist_lag s_wt_glo_lag s_lead_1_lag s_lead_2_lag t
> ime time2 time3 if democracy_1_lag!=. & newregime_1_lag!=1, robust cluster(dirdyadid
> )

Iteration 0:   log pseudolikelihood = -7918.6568  
Iteration 1:   log pseudolikelihood = -6371.1443  
Iteration 2:   log pseudolikelihood = -5556.6253  
Iteration 3:   log pseudolikelihood = -5361.3855  
Iteration 4:   log pseudolikelihood = -5358.0495  
Iteration 5:   log pseudolikelihood = -5358.0424  
Iteration 6:   log pseudolikelihood = -5358.0424  

Logistic regression                             Number of obs     =    557,458
                                                Wald chi2(19)     =    2628.15
                                                Prob > chi2       =     0.0000
Log pseudolikelihood = -5358.0424               Pseudo R2         =     0.3234

                                (Std. Err. adjusted for 24,506 clusters in dirdyadid)
-------------------------------------------------------------------------------------
                    |               Robust
             mzinit |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
--------------------+----------------------------------------------------------------
persrat_2014_1_laga |    .645408   .1849705     3.49   0.000     .2828725    1.007944
  milrat_2014_1_lag |   .7772715   .1715251     4.53   0.000     .4410885    1.113455
      persxmil_laga |  -.2570624   .2836694    -0.91   0.365    -.8130443    .2989194
    democracy_2_lag |  -.0260867   .1324785    -0.20   0.844    -.2857398    .2335664
          cap_1_lag |   4.887201   1.513485     3.23   0.001     1.920824    7.853578
          cap_2_lag |   4.346651   1.670546     2.60   0.009      1.07244    7.620862
      initshare_lag |   .0340184   .1636629     0.21   0.835     -.286755    .3547918
      dependlow_lag |  -28.82757   13.67504    -2.11   0.035    -55.63015   -2.024995
         majmaj_lag |   1.557873   .5337834     2.92   0.004     .5116765    2.604069
         minmaj_lag |   .8990744   .2429975     3.70   0.000     .4228081    1.375341
         majmin_lag |   .8738583   .2442661     3.58   0.000     .3951056    1.352611
      contigdum_lag |    1.44084    .579415     2.49   0.013     .3052071    2.576472
        logdist_lag |  -.2581867   .0721345    -3.58   0.000    -.3995678   -.1168056
       s_wt_glo_lag |  -.9757939   .1616743    -6.04   0.000     -1.29267   -.6589182
       s_lead_1_lag |   .4708916   .1503526     3.13   0.002     .1762058    .7655773
       s_lead_2_lag |   .4321832   .1553474     2.78   0.005      .127708    .7366585
               time |  -34.70405   2.178096   -15.93   0.000    -38.97304   -30.43506
              time2 |   105.5652   10.14093    10.41   0.000     85.68938    125.4411
              time3 |  -94.83411   13.21425    -7.18   0.000    -120.7336   -68.93466
              _cons |  -2.635578   .6083934    -4.33   0.000    -3.828007   -1.443149
-------------------------------------------------------------------------------------

.                         est store weeksA1

.                         logit mzinit persrat_2014_1_laga milrat_2014_1_lag persxmil_
> laga democracy_2_lag cap_1_lag cap_2_lag initshare_lag dependlow_lag majmaj_lag minm
> aj_lag majmin_lag contigdum_lag logdist_lag s_wt_glo_lag s_lead_1_lag s_lead_2_lag t
> ime time2 time3 if democracy_1_lag!=. & newregime_1_lag!=1 & xpers0dem~=., robust cl
> uster(dirdyadid)

Iteration 0:   log pseudolikelihood = -7910.8491  
Iteration 1:   log pseudolikelihood = -6364.0826  
Iteration 2:   log pseudolikelihood = -5551.1039  
Iteration 3:   log pseudolikelihood = -5356.7443  
Iteration 4:   log pseudolikelihood = -5353.4437  
Iteration 5:   log pseudolikelihood = -5353.4368  
Iteration 6:   log pseudolikelihood = -5353.4368  

Logistic regression                             Number of obs     =    556,656
                                                Wald chi2(19)     =    2629.81
                                                Prob > chi2       =     0.0000
Log pseudolikelihood = -5353.4368               Pseudo R2         =     0.3233

                                (Std. Err. adjusted for 24,506 clusters in dirdyadid)
-------------------------------------------------------------------------------------
                    |               Robust
             mzinit |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
--------------------+----------------------------------------------------------------
persrat_2014_1_laga |   .6468146   .1850622     3.50   0.000     .2840994     1.00953
  milrat_2014_1_lag |   .7747398    .172496     4.49   0.000     .4366539    1.112826
      persxmil_laga |  -.2552121    .284376    -0.90   0.369    -.8125788    .3021547
    democracy_2_lag |  -.0271922   .1325298    -0.21   0.837    -.2869458    .2325613
          cap_1_lag |   4.888556    1.51489     3.23   0.001     1.919426    7.857686
          cap_2_lag |   4.256699    1.68042     2.53   0.011      .963135    7.550262
      initshare_lag |   .0321738   .1637457     0.20   0.844    -.2887618    .3531095
      dependlow_lag |  -28.67632   13.64044    -2.10   0.036    -55.41109   -1.941549
         majmaj_lag |   1.563435   .5341633     2.93   0.003     .5164936    2.610375
         minmaj_lag |   .8992267   .2436792     3.69   0.000     .4216243    1.376829
         majmin_lag |    .871356   .2443923     3.57   0.000      .392356    1.350356
      contigdum_lag |   1.446554   .5791784     2.50   0.013     .3113853    2.581723
        logdist_lag |  -.2577803   .0720982    -3.58   0.000    -.3990902   -.1164704
       s_wt_glo_lag |  -.9810712   .1616786    -6.07   0.000    -1.297955   -.6641869
       s_lead_1_lag |   .4690036   .1504382     3.12   0.002     .1741502    .7638569
       s_lead_2_lag |   .4278312   .1556196     2.75   0.006     .1228224      .73284
               time |  -34.68706   2.179294   -15.92   0.000     -38.9584   -30.41572
              time2 |   105.5639   10.14327    10.41   0.000     85.68347    125.4443
              time3 |  -94.86267   13.21338    -7.18   0.000    -120.7604   -68.96491
              _cons |  -2.633883   .6080188    -4.33   0.000    -3.825578   -1.442188
-------------------------------------------------------------------------------------

.                         est store weeksA2

.                         logit mzinit xpers0dem milrat_2014_1_lag xpers0demxmil democ
> racy_2_lag cap_1_lag cap_2_lag initshare_lag dependlow_lag majmaj_lag minmaj_lag maj
> min_lag contigdum_lag logdist_lag s_wt_glo_lag s_lead_1_lag s_lead_2_lag time time2 
> time3 if democracy_1_lag!=. & newregime_1_lag!=1 & xpers0dem~=., robust cluster(dird
> yadid)

Iteration 0:   log pseudolikelihood = -8163.4027  
Iteration 1:   log pseudolikelihood = -6585.5706  
Iteration 2:   log pseudolikelihood =  -5725.943  
Iteration 3:   log pseudolikelihood = -5519.0723  
Iteration 4:   log pseudolikelihood = -5515.3336  
Iteration 5:   log pseudolikelihood = -5515.3236  
Iteration 6:   log pseudolikelihood = -5515.3236  

Logistic regression                             Number of obs     =    587,044
                                                Wald chi2(19)     =    2739.84
                                                Prob > chi2       =     0.0000
Log pseudolikelihood = -5515.3236               Pseudo R2         =     0.3244

                              (Std. Err. adjusted for 24,824 clusters in dirdyadid)
-----------------------------------------------------------------------------------
                  |               Robust
           mzinit |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
------------------+----------------------------------------------------------------
        xpers0dem |   .7608505    .209597     3.63   0.000     .3500479    1.171653
milrat_2014_1_lag |   .7503094    .199366     3.76   0.000     .3595592     1.14106
    xpers0demxmil |  -.2028663   .3650549    -0.56   0.578    -.9183608    .5126282
  democracy_2_lag |  -.0080885    .133864    -0.06   0.952     -.270457    .2542801
        cap_1_lag |    4.64634   1.510433     3.08   0.002     1.685945    7.606734
        cap_2_lag |    4.74819    1.68909     2.81   0.005     1.437634    8.058746
    initshare_lag |   .0335758   .1586643     0.21   0.832    -.2774005    .3445521
    dependlow_lag |  -29.52415   13.48372    -2.19   0.029    -55.95176   -3.096548
       majmaj_lag |   1.609938   .5417922     2.97   0.003     .5480453    2.671832
       minmaj_lag |   .8671619   .2470816     3.51   0.000     .3828908    1.351433
       majmin_lag |   .9651926   .2419077     3.99   0.000     .4910621    1.439323
    contigdum_lag |   1.445152   .5775307     2.50   0.012     .3132125    2.577091
      logdist_lag |  -.2630521   .0718778    -3.66   0.000      -.40393   -.1221743
     s_wt_glo_lag |  -.9699583   .1599869    -6.06   0.000    -1.283527   -.6563898
     s_lead_1_lag |   .4842263   .1510926     3.20   0.001     .1880903    .7803623
     s_lead_2_lag |   .4143375   .1567154     2.64   0.008      .107181     .721494
             time |  -34.72743   2.166961   -16.03   0.000     -38.9746   -30.48027
            time2 |   105.5223   10.08018    10.47   0.000     85.76547     125.279
            time3 |  -94.58435   13.14257    -7.20   0.000    -120.3433   -68.82539
            _cons |  -2.652577   .6106652    -4.34   0.000    -3.849459   -1.455695
-----------------------------------------------------------------------------------

.                         est store weeksA3

.                         xtlogit mzinit persrat_2014_1_laga milrat_2014_1_lag persxmi
> l_laga democracy_2_lag cap_1_lag cap_2_lag initshare_lag dependlow_lag              
>           s_wt_glo_lag s_lead_1_lag s_lead_2_lag time time2 time3 if democracy_1_lag
> !=. & newregime_1_lag!=1, fe
note: multiple positive outcomes within groups encountered.
note: 24,057 groups (541,411 obs) dropped because of all positive or
      all negative outcomes.

Iteration 0:   log likelihood = -2947.2868  
Iteration 1:   log likelihood = -2859.7386  
Iteration 2:   log likelihood =  -2859.591  
Iteration 3:   log likelihood =  -2859.591  

Conditional fixed-effects logistic regression   Number of obs     =     16,047
Group variable: dirdyadid                       Number of groups  =        449

                                                Obs per group:
                                                              min =          4
                                                              avg =       35.7
                                                              max =         50

                                                LR chi2(14)       =     265.74
Log likelihood  =  -2859.591                    Prob > chi2       =     0.0000

-------------------------------------------------------------------------------------
             mzinit |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
--------------------+----------------------------------------------------------------
persrat_2014_1_laga |   .5978244   .2402995     2.49   0.013      .126846    1.068803
  milrat_2014_1_lag |    .758437   .2747324     2.76   0.006     .2199714    1.296903
      persxmil_laga |  -.7259694   .4737572    -1.53   0.125    -1.654517    .2025776
    democracy_2_lag |   .0455701   .1651101     0.28   0.783    -.2780397    .3691799
          cap_1_lag |   -1.09867   2.190634    -0.50   0.616    -5.392233    3.194894
          cap_2_lag |   6.199137   2.370943     2.61   0.009     1.552174     10.8461
      initshare_lag |   2.707752   .7495923     3.61   0.000     1.238579    4.176926
      dependlow_lag |   8.510914   10.57188     0.81   0.421     -12.2096    29.23143
       s_wt_glo_lag |   -.635468   .2964759    -2.14   0.032     -1.21655   -.0543859
       s_lead_1_lag |   .1508824   .3702729     0.41   0.684    -.5748392     .876604
       s_lead_2_lag |  -.9594561   .3658923    -2.62   0.009    -1.676592   -.2423204
               time |  -6.937379    2.01857    -3.44   0.001     -10.8937   -2.981055
              time2 |   1.337251   11.32877     0.12   0.906    -20.86674    23.54124
              time3 |   49.61953   16.77808     2.96   0.003     16.73509    82.50397
-------------------------------------------------------------------------------------

.                         est store weeksA4

.                         xtlogit mzinit persrat_2014_1_laga milrat_2014_1_lag persxmi
> l_laga democracy_2_lag cap_1_lag cap_2_lag initshare_lag dependlow_lag              
>           s_wt_glo_lag s_lead_1_lag s_lead_2_lag time time2 time3 if democracy_1_lag
> !=. & newregime_1_lag!=1 & xpers0dem~=., fe
note: multiple positive outcomes within groups encountered.
note: 24,058 groups (540,649 obs) dropped because of all positive or
      all negative outcomes.

Iteration 0:   log likelihood =  -2943.691  
Iteration 1:   log likelihood = -2856.0373  
Iteration 2:   log likelihood =  -2855.888  
Iteration 3:   log likelihood =  -2855.888  

Conditional fixed-effects logistic regression   Number of obs     =     16,007
Group variable: dirdyadid                       Number of groups  =        448

                                                Obs per group:
                                                              min =          4
                                                              avg =       35.7
                                                              max =         50

                                                LR chi2(14)       =     264.06
Log likelihood  =  -2855.888                    Prob > chi2       =     0.0000

-------------------------------------------------------------------------------------
             mzinit |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
--------------------+----------------------------------------------------------------
persrat_2014_1_laga |   .6007661   .2402858     2.50   0.012     .1298146    1.071718
  milrat_2014_1_lag |   .7607964   .2765002     2.75   0.006     .2188659    1.302727
      persxmil_laga |   -.727206   .4749062    -1.53   0.126    -1.658005     .203593
    democracy_2_lag |    .043079   .1650903     0.26   0.794    -.2804921    .3666501
          cap_1_lag |  -1.083074   2.189611    -0.49   0.621    -5.374634    3.208485
          cap_2_lag |   5.953831   2.376234     2.51   0.012     1.296498    10.61116
      initshare_lag |   2.690346   .7495987     3.59   0.000      1.22116    4.159533
      dependlow_lag |   8.400544   10.57719     0.79   0.427    -12.33037    29.13146
       s_wt_glo_lag |  -.6325321   .2964695    -2.13   0.033    -1.213602   -.0514626
       s_lead_1_lag |   .1542321   .3702883     0.42   0.677    -.5715197    .8799839
       s_lead_2_lag |   -.957224   .3658961    -2.62   0.009    -1.674367   -.2400808
               time |  -6.898209   2.018995    -3.42   0.001    -10.85537   -2.941052
              time2 |   1.226259   11.32947     0.11   0.914    -20.97909    23.43161
              time3 |   49.68247   16.77811     2.96   0.003     16.79797    82.56697
-------------------------------------------------------------------------------------

.                         est store weeksA5

.                         xtlogit mzinit xpers0dem milrat_2014_1_lag xpers0demxmil dem
> ocracy_2_lag cap_1_lag cap_2_lag initshare_lag dependlow_lag                        
> s_wt_glo_lag s_lead_1_lag s_lead_2_lag time time2 time3 if democracy_1_lag!=. & newr
> egime_1_lag!=1 & xpers0dem~=., fe
note: multiple positive outcomes within groups encountered.
note: 24,356 groups (570,467 obs) dropped because of all positive or
      all negative outcomes.

Iteration 0:   log likelihood = -3033.3337  
Iteration 1:   log likelihood = -2944.3636  
Iteration 2:   log likelihood = -2944.1996  
Iteration 3:   log likelihood = -2944.1996  

Conditional fixed-effects logistic regression   Number of obs     =     16,577
Group variable: dirdyadid                       Number of groups  =        468

                                                Obs per group:
                                                              min =          4
                                                              avg =       35.4
                                                              max =         50

                                                LR chi2(14)       =     265.60
Log likelihood  = -2944.1996                    Prob > chi2       =     0.0000

-----------------------------------------------------------------------------------
           mzinit |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
------------------+----------------------------------------------------------------
        xpers0dem |   .3200687   .2535297     1.26   0.207    -.1768403    .8169778
milrat_2014_1_lag |   .5450956     .25167     2.17   0.030     .0518315     1.03836
    xpers0demxmil |  -.4232061   .5001922    -0.85   0.398    -1.403565    .5571525
  democracy_2_lag |  -.0244395   .1628354    -0.15   0.881    -.3435909    .2947119
        cap_1_lag |  -1.449274   2.201141    -0.66   0.510    -5.763431    2.864883
        cap_2_lag |    7.22374    2.33411     3.09   0.002     2.648969    11.79851
    initshare_lag |   3.054534    .729378     4.19   0.000     1.624979    4.484089
    dependlow_lag |   9.288456   10.44756     0.89   0.374    -11.18839     29.7653
     s_wt_glo_lag |  -.6327103   .2968776    -2.13   0.033     -1.21458   -.0508409
     s_lead_1_lag |   .1731221   .3689821     0.47   0.639    -.5500695    .8963137
     s_lead_2_lag |  -1.038512   .3646261    -2.85   0.004    -1.753166   -.3238582
             time |  -7.213275   1.986026    -3.63   0.000    -11.10581   -3.320736
            time2 |   3.118943   11.14713     0.28   0.780    -18.72903    24.96692
            time3 |   46.84975    16.5319     2.83   0.005     14.44783    79.25168
-----------------------------------------------------------------------------------

.                         est store weeksA6

.                         
.                   label var xpers0dem  "G-pers"

.                   label var milrat_2014_1_lag  "W-mil"

.                   label var persrat_2014_1_laga  "W-pers"

.                   label var persxmil_laga  `""W-pers" "x    " "W-mil ""'

.                   label var xpers0demxmil  `""G-pers" "x    " "W-mil ""'

.                         coefplot (weeksA1, msymbol(d)) (weeksA2, msymbol(t)) (weeksA
> 3, msymbol(s)), title("Logit", size(medium)) ///
>                                 scheme(plottig) drop(_cons democracy_2_lag  cap_1_la
> g cap_2_lag initshare_lag dependlow_lag majmaj_lag ///
>                                  minmaj_lag majmin_lag contigdum_lag logdist_lag s_w
> t_glo_lag s_lead_1_lag s_lead_2_lag time time2 time3) xline(0) ///
>                                 grid(glcolor(gs15)) mfcolor(white) xlabel(-2.0 (.5) 
> 2.0)  levels(95 90) xtitle("Coefficient estimate", height(6)) ///
>                                 legend(lab(3 "Original") lab(6 "Orignal-adjusted sam
> ple") lab(9 "G-pers-adjusted sample") size(vsmall) pos(6) ring(1.5) col(3)) ///
>                                 ysize(1) xsize(1.5) saving(r1, replace)         
(file r1.gph saved)

.                         coefplot (weeksA4, msymbol(d)) (weeksA5, msymbol(t)) (weeksA
> 6, msymbol(s)), title("FE-Logit", size(medium)) ///
>                                 scheme(plottig) drop(_cons democracy_2_lag  cap_1_la
> g cap_2_lag initshare_lag dependlow_lag majmaj_lag ///
>                                  minmaj_lag majmin_lag contigdum_lag logdist_lag s_w
> t_glo_lag s_lead_1_lag s_lead_2_lag time time2 time3) xline(0) ///
>                                 grid(glcolor(gs15)) mfcolor(white) xlabel(-2.0 (.5) 
> 2.0)  levels(95 90) xtitle("Coefficient estimate", height(6)) ///
>                                 legend(lab(3 "Original") lab(6 "Orignal-adjusted sam
> ple") lab(9 "G-pers-adjusted sample") size(vsmall) pos(6) ring(1.5) col(3)) ///
>                                 ysize(1) xsize(1.5) saving(r2, replace)         
(file r2.gph saved)

.                         gr combine r1.gph r2.gph, iscale(.8) xsize(6)  

.                         graph export "$dir/golden/MID-D1.pdf", as(pdf) replace
(file /Users/lee/Dropbox/Datavers/golden/MID-D1.pdf written in PDF format)

.                         
.                         
.         ****************************************************************************
> ******************
.         ********** Weeks Figure Appendix B: 2.3.1 and 2.3.2 without the interaction 
> term *************
.         ****************************************************************************
> ******************  
.                 use "$dir/temp-Weeks.dta",clear 

.                 xtset dirdyadid year
       panel variable:  dirdyadid (unbalanced)
        time variable:  year, 1945 to 2000, but with gaps
                delta:  1 unit

.                 global cvar1 = "democracy_2_lag cap_1_lag cap_2_lag initshare_lag de
> pendlow_lag majmaj_lag minmaj_lag majmin_lag contigdum_lag logdist_lag s_wt_glo_lag 
> s_lead_1_lag s_lead_2_lag time time2 time3"

.                 global cvar2 = "democracy_2_lag cap_1_lag cap_2_lag initshare_lag de
> pendlow_lag  s_wt_glo_lag s_lead_1_lag s_lead_2_lag time time2 time3 "

.                 * 2.3.1 logit *
.                 logit mzinit persrat_2014_1_lag milrat_2014_1_lag  $cvar1 ///
>                         if democracy_1_lag==0 & newregime_1_lag!=1, robust cluster(d
> irdyadid)

Iteration 0:   log pseudolikelihood = -4510.2205  
Iteration 1:   log pseudolikelihood = -3954.0983  
Iteration 2:   log pseudolikelihood = -3892.1795  
Iteration 3:   log pseudolikelihood =  -3412.662  
Iteration 4:   log pseudolikelihood = -2924.3629  
Iteration 5:   log pseudolikelihood =  -2892.663  
Iteration 6:   log pseudolikelihood = -2891.6479  
Iteration 7:   log pseudolikelihood = -2891.6447  
Iteration 8:   log pseudolikelihood = -2891.6447  

Logistic regression                             Number of obs     =    268,458
                                                Wald chi2(18)     =    1834.80
                                                Prob > chi2       =     0.0000
Log pseudolikelihood = -2891.6447               Pseudo R2         =     0.3589

                               (Std. Err. adjusted for 12,885 clusters in dirdyadid)
------------------------------------------------------------------------------------
                   |               Robust
            mzinit |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------------+----------------------------------------------------------------
persrat_2014_1_lag |    .743883   .1517056     4.90   0.000     .4465455     1.04122
 milrat_2014_1_lag |   .8184596   .1936753     4.23   0.000     .4388629    1.198056
   democracy_2_lag |   .4647827   .1452835     3.20   0.001     .1800323    .7495332
         cap_1_lag |     10.893   5.374845     2.03   0.043     .3585016     21.4275
         cap_2_lag |   3.753298   1.810823     2.07   0.038     .2041507    7.302444
     initshare_lag |  -.0171529   .2083363    -0.08   0.934    -.4254845    .3911787
     dependlow_lag |   15.23925   3.751576     4.06   0.000     7.886299    22.59221
        majmaj_lag |   .8055016   .8799071     0.92   0.360    -.9190847    2.530088
        minmaj_lag |    1.35324   .2781024     4.87   0.000      .808169    1.898311
        majmin_lag |  -.0314193   .7047237    -0.04   0.964    -1.412652    1.349814
     contigdum_lag |   2.187866   .6218891     3.52   0.000     .9689856    3.406746
       logdist_lag |  -.1602841   .0747794    -2.14   0.032     -.306849   -.0137192
      s_wt_glo_lag |  -.0706758    .312895    -0.23   0.821    -.6839387    .5425872
      s_lead_1_lag |  -.4209885   .3620343    -1.16   0.245    -1.130563    .2885856
      s_lead_2_lag |   .9212601    .263042     3.50   0.000     .4057073    1.436813
              time |  -33.09012   2.974625   -11.12   0.000    -38.92028   -27.25996
             time2 |   108.5794   16.95877     6.40   0.000     75.34081     141.818
             time3 |  -118.2839   27.24269    -4.34   0.000    -171.6786   -64.88923
             _cons |  -4.597908    .711259    -6.46   0.000     -5.99195   -3.203866
------------------------------------------------------------------------------------

.                         est store weeksB1

.                 * 2.3.1 logit Sample with xpers not missing *
.                 logit mzinit persrat_2014_1_lag milrat_2014_1_lag  $cvar1 ///
>                         if democracy_1_lag==0 & newregime_1_lag!=1 & xpers~=. & pers
> rat_2014_1_lag~=.,  robust cluster(dirdyadid)

Iteration 0:   log pseudolikelihood = -4502.2676  
Iteration 1:   log pseudolikelihood = -3945.7376  
Iteration 2:   log pseudolikelihood = -3868.2675  
Iteration 3:   log pseudolikelihood = -3389.4305  
Iteration 4:   log pseudolikelihood =  -2917.553  
Iteration 5:   log pseudolikelihood = -2888.4769  
Iteration 6:   log pseudolikelihood = -2887.5986  
Iteration 7:   log pseudolikelihood =  -2887.596  
Iteration 8:   log pseudolikelihood =  -2887.596  

Logistic regression                             Number of obs     =    267,656
                                                Wald chi2(18)     =    1834.77
                                                Prob > chi2       =     0.0000
Log pseudolikelihood =  -2887.596               Pseudo R2         =     0.3586

                               (Std. Err. adjusted for 12,879 clusters in dirdyadid)
------------------------------------------------------------------------------------
                   |               Robust
            mzinit |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------------+----------------------------------------------------------------
persrat_2014_1_lag |   .7487707   .1522573     4.92   0.000     .4503519    1.047189
 milrat_2014_1_lag |    .816783    .193562     4.22   0.000     .4374085    1.196158
   democracy_2_lag |   .4643217   .1452753     3.20   0.001     .1795875     .749056
         cap_1_lag |   10.87522   5.380923     2.02   0.043     .3288001    21.42163
         cap_2_lag |   3.648837   1.824641     2.00   0.046     .0726065    7.225068
     initshare_lag |  -.0173199   .2084902    -0.08   0.934    -.4259531    .3913133
     dependlow_lag |   15.26639   3.763205     4.06   0.000     7.890646    22.64214
        majmaj_lag |   .8105564   .8811195     0.92   0.358     -.916406    2.537519
        minmaj_lag |   1.351582   .2793999     4.84   0.000     .8039678    1.899195
        majmin_lag |  -.0359006   .7053149    -0.05   0.959    -1.418292    1.346491
     contigdum_lag |   2.197672   .6215631     3.54   0.000     .9794307    3.415913
       logdist_lag |  -.1596549   .0746994    -2.14   0.033    -.3060631   -.0132467
      s_wt_glo_lag |  -.0856491   .3128117    -0.27   0.784    -.6987488    .5274505
      s_lead_1_lag |  -.4236542   .3621088    -1.17   0.242    -1.133374     .286066
      s_lead_2_lag |   .9112074   .2633213     3.46   0.001     .3951072    1.427308
              time |  -33.05248   2.975617   -11.11   0.000    -38.88458   -27.22037
             time2 |   108.5086   16.95649     6.40   0.000     75.27452    141.7427
             time3 |  -118.2056   27.23455    -4.34   0.000    -171.5843   -64.82685
             _cons |  -4.594832   .7109511    -6.46   0.000    -5.988271   -3.201394
------------------------------------------------------------------------------------

.                         est store weeksB2       

.                 * 2.3.1 logit Sample with xpers not missing & xpers instead of persr
> at *
.                 logit mzinit xpers milrat_2014_1_lag  $cvar1 ///
>                         if democracy_1_lag==0 & newregime_1_lag!=1 & xpers~=. & pers
> rat_2014_1_lag~=.,  robust cluster(dirdyadid)

Iteration 0:   log pseudolikelihood = -4502.2676  
Iteration 1:   log pseudolikelihood = -3944.0882  
Iteration 2:   log pseudolikelihood = -3867.1556  
Iteration 3:   log pseudolikelihood = -3385.3373  
Iteration 4:   log pseudolikelihood = -2913.9951  
Iteration 5:   log pseudolikelihood = -2885.4488  
Iteration 6:   log pseudolikelihood = -2884.6664  
Iteration 7:   log pseudolikelihood = -2884.6645  
Iteration 8:   log pseudolikelihood = -2884.6645  

Logistic regression                             Number of obs     =    267,656
                                                Wald chi2(18)     =    1875.38
                                                Prob > chi2       =     0.0000
Log pseudolikelihood = -2884.6645               Pseudo R2         =     0.3593

                              (Std. Err. adjusted for 12,879 clusters in dirdyadid)
-----------------------------------------------------------------------------------
                  |               Robust
           mzinit |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
------------------+----------------------------------------------------------------
            xpers |   .9926282   .1740452     5.70   0.000     .6515058    1.333751
milrat_2014_1_lag |   .7346396   .1894802     3.88   0.000     .3632652    1.106014
  democracy_2_lag |    .491341   .1490352     3.30   0.001     .1992375    .7834446
        cap_1_lag |    6.58437   5.391221     1.22   0.222    -3.982229    17.15097
        cap_2_lag |   3.760472   1.803081     2.09   0.037     .2264979    7.294447
    initshare_lag |  -.1378705   .2077651    -0.66   0.507    -.5450826    .2693416
    dependlow_lag |   13.62609   4.143631     3.29   0.001     5.504722    21.74746
       majmaj_lag |   1.499745   .8918155     1.68   0.093    -.2481808    3.247672
       minmaj_lag |    1.30556   .2725755     4.79   0.000     .7713218    1.839798
       majmin_lag |    .702368   .6988836     1.00   0.315    -.6674187    2.072155
    contigdum_lag |    2.20578   .5992944     3.68   0.000     1.031185    3.380376
      logdist_lag |  -.1573942   .0720267    -2.19   0.029    -.2985639   -.0162245
     s_wt_glo_lag |   -.031671   .3079629    -0.10   0.918    -.6352672    .5719252
     s_lead_1_lag |  -.4138406   .3632806    -1.14   0.255    -1.125858    .2981763
     s_lead_2_lag |   .9176141   .2616049     3.51   0.000      .404878     1.43035
             time |  -32.17214   2.955921   -10.88   0.000    -37.96564   -26.37864
            time2 |   102.5288   16.77882     6.11   0.000     69.64289    135.4147
            time3 |  -109.5134   26.91021    -4.07   0.000    -162.2564   -56.77035
            _cons |  -4.608241   .6968882    -6.61   0.000    -5.974116   -3.242365
-----------------------------------------------------------------------------------

.                         est store weeksB3       

.                 * 2.3.2 FE logit *
.                 xtlogit mzinit persrat_2014_1_lag milrat_2014_1_lag  democracy_2_lag
>  ///
>                         cap_1_lag cap_2_lag initshare_lag dependlow_lag  s_wt_glo_la
> g s_lead_1_lag s_lead_2_lag ///
>                         time time2 time3 if democracy_1_lag==0 & newregime_1_lag!=1,
>  fe
note: multiple positive outcomes within groups encountered.
note: 12,620 groups (260,280 obs) dropped because of all positive or
      all negative outcomes.

Iteration 0:   log likelihood = -1661.5487  
Iteration 1:   log likelihood = -1608.9627  
Iteration 2:   log likelihood = -1608.6447  
Iteration 3:   log likelihood = -1608.6447  

Conditional fixed-effects logistic regression   Number of obs     =      8,178
Group variable: dirdyadid                       Number of groups  =        265

                                                Obs per group:
                                                              min =          4
                                                              avg =       30.9
                                                              max =         49

                                                LR chi2(13)       =     102.42
Log likelihood  = -1608.6447                    Prob > chi2       =     0.0000

------------------------------------------------------------------------------------
            mzinit |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------------+----------------------------------------------------------------
persrat_2014_1_lag |   .6259849   .2454943     2.55   0.011      .144825    1.107145
 milrat_2014_1_lag |   .6428413   .3362459     1.91   0.056    -.0161886    1.301871
   democracy_2_lag |  -.1124959   .2393951    -0.47   0.638    -.5817016    .3567099
         cap_1_lag |   .8840856   10.65067     0.08   0.934    -19.99084    21.75901
         cap_2_lag |   9.063212   2.886359     3.14   0.002     3.406052    14.72037
     initshare_lag |   .4132235   1.049026     0.39   0.694    -1.642829    2.469276
     dependlow_lag |   27.46229   16.83414     1.63   0.103    -5.532022     60.4566
      s_wt_glo_lag |  -.0832124   .5027254    -0.17   0.869    -1.068536    .9021113
      s_lead_1_lag |  -1.675514   .5722843    -2.93   0.003     -2.79717    -.553857
      s_lead_2_lag |   .1518413   .5583324     0.27   0.786    -.9424701    1.246153
              time |  -8.112541   3.096712    -2.62   0.009    -14.18198   -2.043098
             time2 |   17.82452    19.9773     0.89   0.372    -21.33027    56.97932
             time3 |    31.4222   34.19671     0.92   0.358    -35.60212    98.44653
------------------------------------------------------------------------------------

.                         est store weeksB4       

.                 * 2.3.2 FE logit reduced sample with xpers not missing *
.                 xtlogit mzinit persrat_2014_1_lag milrat_2014_1_lag  $cvar2 ///
>                         if democracy_1_lag==0 & newregime_1_lag!=1 & xpers~=. & pers
> rat_2014_1_lag~=., fe
note: multiple positive outcomes within groups encountered.
note: 12,615 groups (259,498 obs) dropped because of all positive or
      all negative outcomes.

Iteration 0:   log likelihood = -1658.5232  
Iteration 1:   log likelihood = -1605.9135  
Iteration 2:   log likelihood = -1605.5915  
Iteration 3:   log likelihood = -1605.5914  

Conditional fixed-effects logistic regression   Number of obs     =      8,158
Group variable: dirdyadid                       Number of groups  =        264

                                                Obs per group:
                                                              min =          4
                                                              avg =       30.9
                                                              max =         49

                                                LR chi2(13)       =     102.15
Log likelihood  = -1605.5914                    Prob > chi2       =     0.0000

------------------------------------------------------------------------------------
            mzinit |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------------+----------------------------------------------------------------
persrat_2014_1_lag |   .6343984   .2456117     2.58   0.010     .1530084    1.115788
 milrat_2014_1_lag |   .6757119   .3373332     2.00   0.045      .014551    1.336873
   democracy_2_lag |   -.110046   .2392013    -0.46   0.645    -.5788718    .3587798
         cap_1_lag |    1.03473   10.65464     0.10   0.923    -19.84799    21.91745
         cap_2_lag |   8.859943   2.894334     3.06   0.002     3.187153    14.53273
     initshare_lag |     .41605   1.048831     0.40   0.692    -1.639621    2.471721
     dependlow_lag |   27.46249   16.86588     1.63   0.103    -5.594023      60.519
      s_wt_glo_lag |  -.0527709   .5027012    -0.10   0.916    -1.038047    .9325053
      s_lead_1_lag |  -1.696081   .5723767    -2.96   0.003    -2.817919   -.5742433
      s_lead_2_lag |   .1855433   .5583481     0.33   0.740     -.908799    1.279886
              time |  -8.092296   3.097559    -2.61   0.009     -14.1634   -2.021191
             time2 |   17.70332   19.97852     0.89   0.376    -21.45385    56.86049
             time3 |   31.67465   34.19799     0.93   0.354    -35.35218    98.70147
------------------------------------------------------------------------------------

.                         est store weeksB5

.                 * 2.3.2 FE logit reduced sample with xpers not missing & xpers inste
> ad of persrat *
.                 xtlogit mzinit xpers milrat_2014_1_lag  $cvar2 if democracy_1_lag==0
>  & ///
>                         newregime_1_lag!=1 & xpers~=. & persrat_2014_1_lag~=., fe
note: multiple positive outcomes within groups encountered.
note: 12,615 groups (259,498 obs) dropped because of all positive or
      all negative outcomes.

Iteration 0:   log likelihood = -1660.2866  
Iteration 1:   log likelihood = -1608.0591  
Iteration 2:   log likelihood = -1607.7439  
Iteration 3:   log likelihood = -1607.7439  

Conditional fixed-effects logistic regression   Number of obs     =      8,158
Group variable: dirdyadid                       Number of groups  =        264

                                                Obs per group:
                                                              min =          4
                                                              avg =       30.9
                                                              max =         49

                                                LR chi2(13)       =      97.84
Log likelihood  = -1607.7439                    Prob > chi2       =     0.0000

-----------------------------------------------------------------------------------
           mzinit |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
------------------+----------------------------------------------------------------
            xpers |   .3493853   .2262786     1.54   0.123    -.0941126    .7928831
milrat_2014_1_lag |   .6039619   .3413229     1.77   0.077    -.0650187    1.272942
  democracy_2_lag |  -.1628068   .2382467    -0.68   0.494    -.6297619    .3041482
        cap_1_lag |  -5.255957    10.2285    -0.51   0.607    -25.30345    14.79153
        cap_2_lag |   9.376708   2.875138     3.26   0.001     3.741541    15.01187
    initshare_lag |   .3724901   1.056793     0.35   0.724    -1.698786    2.443767
    dependlow_lag |    26.6166   16.30383     1.63   0.103    -5.338322    58.57152
     s_wt_glo_lag |  -.1687188   .5024322    -0.34   0.737    -1.153468    .8160303
     s_lead_1_lag |  -1.511644   .5661026    -2.67   0.008    -2.621184   -.4021031
     s_lead_2_lag |   .0248016   .5553628     0.04   0.964    -1.063689    1.113293
             time |  -7.701467   3.088493    -2.49   0.013     -13.7548   -1.648132
            time2 |   13.43934   19.84068     0.68   0.498    -25.44769    52.32636
            time3 |   39.65339    33.9318     1.17   0.243    -26.85172    106.1585
-----------------------------------------------------------------------------------

.                         est store weeksB6

.               label var xpers  "G-pers"

.                   label var milrat_2014_1_lag  "W-mil"

.                   label var persrat_2014_1_lag  "W-pers"

.                 coefplot (weeksB1, msymbol(d)) (weeksB2, msymbol(t)) (weeksB3, msymb
> ol(s)), title("Logit", size(medium)) ///
>                         scheme(plottig) drop(_cons democracy_2_lag  cap_1_lag cap_2_
> lag initshare_lag dependlow_lag majmaj_lag ///
>                          minmaj_lag majmin_lag contigdum_lag logdist_lag s_wt_glo_la
> g s_lead_1_lag s_lead_2_lag time time2 time3) xline(0) ///
>                         grid(glcolor(gs15)) mfcolor(white) xlabel(-2.0 (.5) 2.0)  le
> vels(95 90) xtitle("Coefficient estimate", height(6)) ///
>                         legend(lab(3 "Original") lab(6 "Orignal-adjusted sample") la
> b(9 "G-pers-adjusted sample") size(vsmall)pos(6) ring(1.5) col(3)) ///
>                         ysize(1) xsize(1.5) saving(r1, replace)                 
(file r1.gph saved)

.                 coefplot (weeksB4, msymbol(d)) (weeksB5, msymbol(t)) (weeksB6, msymb
> ol(s)) , title("FE-Logit", size(medium)) ///
>                         scheme(plottig) drop(_cons democracy_2_lag  cap_1_lag cap_2_
> lag initshare_lag dependlow_lag majmaj_lag ///
>                          minmaj_lag majmin_lag contigdum_lag logdist_lag s_wt_glo_la
> g s_lead_1_lag s_lead_2_lag time time2 time3) xline(0) ///
>                         grid(glcolor(gs15)) mfcolor(white) xlabel(-2.0 (.5) 2.0)  le
> vels(95 90) xtitle("Coefficient estimate", height(6)) ///
>                         legend(lab(3 "Original") lab(6 "Orignal-adjusted sample") la
> b(9 "G-pers-adjusted sample") size(vsmall)pos(6) ring(1.5) col(3)) ///
>                         ysize(1) xsize(1.5) saving(r2, replace)         
(file r2.gph saved)

.                 gr combine r1.gph r2.gph, iscale(.8) xsize(6)  

.                 graph export "$dir/golden/MID-D2.pdf", as(pdf) replace
(file /Users/lee/Dropbox/Datavers/golden/MID-D2.pdf written in PDF format)

.                 
.                 
.                 est restore weeksB6
(results weeksB6 are active now)

.                 tab milrat_2014_1_lag if e(sample)

      W-mil |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |      2,396       29.37       29.37
         .2 |        396        4.85       34.22
        .25 |      1,535       18.82       53.04
   .3333333 |        801        9.82       62.86
         .4 |        116        1.42       64.28
         .5 |         66        0.81       65.09
         .6 |        816       10.00       75.09
   .6666667 |        208        2.55       77.64
        .75 |        186        2.28       79.92
         .8 |        846       10.37       90.29
          1 |        792        9.71      100.00
------------+-----------------------------------
      Total |      8,158      100.00

.                 margins, dydx(xpers) at(milrat_2014_1_lag=(0(.05)1)) vsquish post

Average marginal effects                        Number of obs     =      8,158
Model VCE    : OIM

Expression   : Pr(mzinit|fixed effect is 0), predict(pu0)
dy/dx w.r.t. : xpers
1._at        : milrat_201~g    =           0
2._at        : milrat_201~g    =         .05
3._at        : milrat_201~g    =          .1
4._at        : milrat_201~g    =         .15
5._at        : milrat_201~g    =          .2
6._at        : milrat_201~g    =         .25
7._at        : milrat_201~g    =          .3
8._at        : milrat_201~g    =         .35
9._at        : milrat_201~g    =          .4
10._at       : milrat_201~g    =         .45
11._at       : milrat_201~g    =          .5
12._at       : milrat_201~g    =         .55
13._at       : milrat_201~g    =          .6
14._at       : milrat_201~g    =         .65
15._at       : milrat_201~g    =          .7
16._at       : milrat_201~g    =         .75
17._at       : milrat_201~g    =          .8
18._at       : milrat_201~g    =         .85
19._at       : milrat_201~g    =          .9
20._at       : milrat_201~g    =         .95
21._at       : milrat_201~g    =           1

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
xpers        |
         _at |
          1  |   .0786137   .0509092     1.54   0.123    -.0211665    .1783939
          2  |   .0786749   .0508326     1.55   0.122    -.0209552     .178305
          3  |   .0787081   .0507439     1.55   0.121    -.0207482    .1781643
          4  |   .0787129   .0506437     1.55   0.120    -.0205468    .1779727
          5  |   .0786894   .0505325     1.56   0.119    -.0203524    .1777313
          6  |   .0786375   .0504111     1.56   0.119    -.0201665    .1774416
          7  |   .0785572   .0502804     1.56   0.118    -.0199905     .177105
          8  |   .0784486    .050141     1.56   0.118     -.019826    .1767231
          9  |   .0783116   .0499939     1.57   0.117    -.0196748    .1762979
         10  |   .0781463   .0498401     1.57   0.117    -.0195384    .1758311
         11  |   .0779531   .0496804     1.57   0.117    -.0194186    .1753248
         12  |   .0777319   .0495158     1.57   0.116    -.0193173    .1747812
         13  |   .0774831   .0493475     1.57   0.116    -.0192361    .1742024
         14  |    .077207   .0491764     1.57   0.116    -.0191769    .1735909
         15  |   .0769037   .0490035     1.57   0.117    -.0191413    .1729488
         16  |   .0765738   .0488299     1.57   0.117    -.0191311    .1722786
         17  |   .0762175   .0486567     1.57   0.117    -.0191479    .1715828
         18  |   .0758352   .0484848     1.56   0.118    -.0191932    .1708636
         19  |   .0754275   .0483152     1.56   0.118    -.0192685    .1701235
         20  |   .0749947   .0481488     1.56   0.119    -.0193752    .1693647
         21  |   .0745375   .0479866     1.55   0.120    -.0195145    .1685896
------------------------------------------------------------------------------

.                 matrix at=e(at)

.                 matrix at=at[1...,"milrat_2014_1_lag"]

.                 matrix list at

at[21,1]
        milrat_201~g
 1._at             0
 2._at           .05
 3._at            .1
 4._at           .15
 5._at            .2
 6._at           .25
 7._at            .3
 8._at           .35
 9._at            .4
10._at           .45
11._at            .5
12._at           .55
13._at            .6
14._at           .65
15._at            .7
16._at           .75
17._at            .8
18._at           .85
19._at            .9
20._at           .95
21._at             1

.                 parmest, norestore

.                 svmat at

.                 twoway (line min95 at1, lpattern(dash) lcol(blue)) (line estimate at
> 1,lcol(blue)) ///
>                         (line max95 at1,lcol(blue) lpattern(dash)), legend(order (1 
> "Upper 95% c.i." 3 "Lower 95% c.i.")) ///
>                         yline(0,lcol(gs5))  xtitle(Score on militarism index) title(
> W-personalism) legend(pos(6) col(2)) ///
>                         ytitle(Marginal effect of personalism) saving(r1.gph,replace
> ) scheme(plottig)ylab(-.1(.1).4)
(file r1.gph saved)

. 
.         ****************************************************************************
> ******
.         ********** Weeks Figure Appendix C: substituting other pers measures *******
> ******
.         ****************************************************************************
> ******
.                         use "$dir/temp-Weeks.dta",clear 

.                         xtset dirdyadid year
       panel variable:  dirdyadid (unbalanced)
        time variable:  year, 1945 to 2000, but with gaps
                delta:  1 unit

.                         * 2.3.2 FE logit reduced sample with xpers not missing & xpe
> rs instead of persrat *
.                         xtlogit mzinit irtweeks milrat_2014_1_lag irtweeksx $cvar2 i
> f democracy_1_lag==0 & ///
>                                 newregime_1_lag!=1 & xpers~=. & persrat_2014_1_lag~=
> ., fe
note: multiple positive outcomes within groups encountered.
note: 12,615 groups (259,498 obs) dropped because of all positive or
      all negative outcomes.

Iteration 0:   log likelihood = -1660.6485  
Iteration 1:   log likelihood = -1608.4261  
Iteration 2:   log likelihood = -1608.1295  
Iteration 3:   log likelihood = -1608.1294  

Conditional fixed-effects logistic regression   Number of obs     =      8,158
Group variable: dirdyadid                       Number of groups  =        264

                                                Obs per group:
                                                              min =          4
                                                              avg =       30.9
                                                              max =         49

                                                LR chi2(14)       =      97.07
Log likelihood  = -1608.1294                    Prob > chi2       =     0.0000

-----------------------------------------------------------------------------------
           mzinit |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
------------------+----------------------------------------------------------------
         irtweeks |   .3719587   .3790548     0.98   0.326    -.3709749    1.114892
milrat_2014_1_lag |   .6789855   .5135332     1.32   0.186    -.3275211    1.685492
 irtweeksxmil_lag |  -.1134146    .718986    -0.16   0.875    -1.522601    1.295772
  democracy_2_lag |  -.1662385   .2383758    -0.70   0.486    -.6334465    .3009695
        cap_1_lag |  -5.612524   10.24142    -0.55   0.584    -25.68534    14.46029
        cap_2_lag |   9.426662   2.873809     3.28   0.001       3.7941    15.05922
    initshare_lag |   .4117894    1.05583     0.39   0.697      -1.6576    2.481179
    dependlow_lag |   26.61595    16.2825     1.63   0.102    -5.297171    58.52906
     s_wt_glo_lag |  -.1853105   .5025067    -0.37   0.712    -1.170205    .7995844
     s_lead_1_lag |  -1.494926   .5655938    -2.64   0.008    -2.603469    -.386382
     s_lead_2_lag |   .0293337   .5593507     0.05   0.958    -1.066974    1.125641
             time |  -7.717742   3.089351    -2.50   0.012    -13.77276   -1.662726
            time2 |   13.52431   19.85072     0.68   0.496    -25.38239    52.43102
            time3 |    39.4118   33.95146     1.16   0.246    -27.13184    105.9554
-----------------------------------------------------------------------------------

.                                 est store weeksC1

.                         * 2.3.2 FE logit reduced sample with xpers not missing & xpe
> rs instead of persrat *
.                         xtlogit mzinit irtpers11 milrat_2014_1_lag irtpers11x $cvar2
>  if democracy_1_lag==0 & ///
>                                 newregime_1_lag!=1 & xpers~=. & persrat_2014_1_lag~=
> ., fe
note: multiple positive outcomes within groups encountered.
note: 12,615 groups (259,498 obs) dropped because of all positive or
      all negative outcomes.

Iteration 0:   log likelihood =  -1659.949  
Iteration 1:   log likelihood = -1607.9078  
Iteration 2:   log likelihood = -1607.6135  
Iteration 3:   log likelihood = -1607.6135  

Conditional fixed-effects logistic regression   Number of obs     =      8,158
Group variable: dirdyadid                       Number of groups  =        264

                                                Obs per group:
                                                              min =          4
                                                              avg =       30.9
                                                              max =         49

                                                LR chi2(14)       =      98.11
Log likelihood  = -1607.6135                    Prob > chi2       =     0.0000

-----------------------------------------------------------------------------------
           mzinit |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
------------------+----------------------------------------------------------------
        irtpers11 |   .5340345   .3957087     1.35   0.177    -.2415403    1.309609
milrat_2014_1_lag |    .759778   .5195928     1.46   0.144    -.2586052    1.778161
irtpers11xmil_lag |  -.2850836   .8129141    -0.35   0.726    -1.878366    1.308199
  democracy_2_lag |  -.1637766   .2385559    -0.69   0.492    -.6313375    .3037844
        cap_1_lag |  -5.929273   10.28417    -0.58   0.564    -26.08587    14.22733
        cap_2_lag |    9.37674   2.878356     3.26   0.001     3.735265    15.01821
    initshare_lag |   .3750301   1.056897     0.35   0.723    -1.696451    2.446511
    dependlow_lag |   26.91817   16.38242     1.64   0.100    -5.190778    59.02712
     s_wt_glo_lag |  -.1696815   .5028653    -0.34   0.736    -1.155279    .8159163
     s_lead_1_lag |  -1.501756   .5670222    -2.65   0.008    -2.613099   -.3904126
     s_lead_2_lag |   .0569491    .559424     0.10   0.919    -1.039502      1.1534
             time |   -7.74284    3.08924    -2.51   0.012    -13.79764   -1.688041
            time2 |   13.89106   19.85402     0.70   0.484     -25.0221    52.80423
            time3 |   38.92665   33.95921     1.15   0.252    -27.63218    105.4855
-----------------------------------------------------------------------------------

.                                 est store weeksC2

.                         * 2.3.2 FE logit reduced sample with xpers not missing & xpe
> rs instead of persrat *
.                         xtlogit mzinit irtpers10 milrat_2014_1_lag irtpers10x $cvar2
>  if democracy_1_lag==0 & ///
>                                 newregime_1_lag!=1 & xpers~=. & persrat_2014_1_lag~=
> ., fe
note: multiple positive outcomes within groups encountered.
note: 12,615 groups (259,498 obs) dropped because of all positive or
      all negative outcomes.

Iteration 0:   log likelihood = -1659.6114  
Iteration 1:   log likelihood = -1607.5309  
Iteration 2:   log likelihood = -1607.2385  
Iteration 3:   log likelihood = -1607.2384  

Conditional fixed-effects logistic regression   Number of obs     =      8,158
Group variable: dirdyadid                       Number of groups  =        264

                                                Obs per group:
                                                              min =          4
                                                              avg =       30.9
                                                              max =         49

                                                LR chi2(14)       =      98.86
Log likelihood  = -1607.2384                    Prob > chi2       =     0.0000

-----------------------------------------------------------------------------------
           mzinit |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
------------------+----------------------------------------------------------------
        irtpers10 |   .5928136    .380065     1.56   0.119    -.1521001    1.337727
milrat_2014_1_lag |   .7957923   .5087106     1.56   0.118    -.2012621    1.792847
irtpers10xmil_lag |  -.3489728   .7693129    -0.45   0.650    -1.856798    1.158853
  democracy_2_lag |  -.1621289    .238711    -0.68   0.497    -.6299939     .305736
        cap_1_lag |  -5.955767   10.29425    -0.58   0.563    -26.13213     14.2206
        cap_2_lag |   9.332919   2.881406     3.24   0.001     3.685467    14.98037
    initshare_lag |   .3474588   1.057712     0.33   0.743    -1.725618    2.420535
    dependlow_lag |   26.96961   16.40473     1.64   0.100    -5.183072     59.1223
     s_wt_glo_lag |  -.1565133   .5027825    -0.31   0.756    -1.141949    .8289223
     s_lead_1_lag |   -1.51266   .5672679    -2.67   0.008    -2.624484   -.4008349
     s_lead_2_lag |   .0648655   .5593221     0.12   0.908    -1.031386    1.161117
             time |  -7.730179   3.088999    -2.50   0.012    -13.78451   -1.675853
            time2 |   13.90356   19.84872     0.70   0.484    -24.99921    52.80634
            time3 |   38.90851    33.9514     1.15   0.252    -27.63501     105.452
-----------------------------------------------------------------------------------

.                                 est store weeksC3

. 
.                   label var irtweeks  "G-pers (W)"

.                   label var milrat_2014_1_lag  "W-mil"

.                   label var irtpers11  "G-pers (11)"

.                   label var irtpers10  "G-pers (10)"

.                   label var irtweeksx  `""G-pers (W)" "x       " "W-mil   ""'

.                   label var irtpers11x  `""G-pers (11)" "x        " "W-mil    ""'

.                   label var irtpers10x  `""G-pers (10)" "x        " "W-mil    ""'   
>     

.                         coefplot (weeksC1, msymbol(d)) (weeksC2, msymbol(t)) (weeksC
> 3, msymbol(s)) , title("FE-Logit", size(medium)) ///
>                                 scheme(plottig) drop(_cons democracy_2_lag  cap_1_la
> g cap_2_lag initshare_lag dependlow_lag majmaj_lag ///
>                                 order (milrat_2014_1_lag) ///
>                                  minmaj_lag majmin_lag contigdum_lag logdist_lag s_w
> t_glo_lag s_lead_1_lag s_lead_2_lag time time2 time3) xline(0) ///
>                                 grid(glcolor(gs15)) mfcolor(white) xlabel(-2 (.5) 2)
>   levels(95 90) xtitle("Coefficient estimate", height(6)) ///
>                                 legend(lab(3 "G-pers-IRT-Weeks") lab(6 "G-pers-IRT-1
> 1") lab(9 "G-pers-IRT-10") pos(6) ring(1.5) col(3)) ///
>                                 ysize(1) xsize(1.5) saving(r3, replace) 
(note: file r3.gph not found)
(file r3.gph saved)

.                         graph export "$dir/golden/MID-D3.pdf", as(pdf) replace
(file /Users/lee/Dropbox/Datavers/golden/MID-D3.pdf written in PDF format)

.                         
.         ****************************************************************************
> ******
.         ********** Weeks Figure Appendix D: add new regimes into the sample  *******
> ******
.         ****************************************************************************
> ******      
.                 use "$dir/temp-Weeks.dta",clear 

.                 xtset dirdyadid year
       panel variable:  dirdyadid (unbalanced)
        time variable:  year, 1945 to 2000, but with gaps
                delta:  1 unit

.                 global cvar1 = "democracy_2_lag cap_1_lag cap_2_lag initshare_lag de
> pendlow_lag majmaj_lag minmaj_lag majmin_lag contigdum_lag logdist_lag s_wt_glo_lag 
> s_lead_1_lag s_lead_2_lag time time2 time3"

.                 global cvar2 = "democracy_2_lag cap_1_lag cap_2_lag initshare_lag de
> pendlow_lag  s_wt_glo_lag s_lead_1_lag s_lead_2_lag time time2 time3 "

.                 * 2.3.1 logit *
.                 logit mzinit persrat_2014_1_lag milrat_2014_1_lag persxmil_lag $cvar
> 1 ///
>                         if democracy_1_lag==0, robust cluster(dirdyadid)

Iteration 0:   log pseudolikelihood = -5475.2902  
Iteration 1:   log pseudolikelihood = -4772.7916  
Iteration 2:   log pseudolikelihood = -4487.5559  
Iteration 3:   log pseudolikelihood = -4183.2409  
Iteration 4:   log pseudolikelihood = -3602.2765  
Iteration 5:   log pseudolikelihood = -3543.8238  
Iteration 6:   log pseudolikelihood = -3540.7476  
Iteration 7:   log pseudolikelihood = -3540.7346  
Iteration 8:   log pseudolikelihood = -3540.7346  

Logistic regression                             Number of obs     =    323,783
                                                Wald chi2(19)     =    2028.35
                                                Prob > chi2       =     0.0000
Log pseudolikelihood = -3540.7346               Pseudo R2         =     0.3533

                               (Std. Err. adjusted for 13,399 clusters in dirdyadid)
------------------------------------------------------------------------------------
                   |               Robust
            mzinit |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------------+----------------------------------------------------------------
persrat_2014_1_lag |   1.003531   .2152702     4.66   0.000     .5816089    1.425453
 milrat_2014_1_lag |   1.182088   .2540702     4.65   0.000     .6841198    1.680057
      persxmil_lag |  -.8561007   .2955455    -2.90   0.004    -1.435359    -.276842
   democracy_2_lag |   .5314022   .1502417     3.54   0.000     .2369339    .8258705
         cap_1_lag |   11.14644   5.347442     2.08   0.037     .6656469    21.62723
         cap_2_lag |   3.444428    1.65086     2.09   0.037     .2088021    6.680054
     initshare_lag |  -.0041416   .1952921    -0.02   0.983    -.3869071    .3786239
     dependlow_lag |   12.78729   3.789726     3.37   0.001     5.359566    20.21502
        majmaj_lag |    .778192   .8954994     0.87   0.385    -.9769545    2.533339
        minmaj_lag |     1.2975   .2631947     4.93   0.000     .7816477    1.813352
        majmin_lag |   -.035782   .7021045    -0.05   0.959    -1.411881    1.340317
     contigdum_lag |   2.081423    .619933     3.36   0.001     .8663767    3.296469
       logdist_lag |  -.1853019   .0744041    -2.49   0.013    -.3311312   -.0394727
      s_wt_glo_lag |  -.3297915   .2592093    -1.27   0.203    -.8378324    .1782495
      s_lead_1_lag |  -.2333897   .2913382    -0.80   0.423    -.8044021    .3376228
      s_lead_2_lag |   .7640598   .2268073     3.37   0.001     .3195257    1.208594
              time |  -34.76731   2.984112   -11.65   0.000    -40.61606   -28.91856
             time2 |   119.0532   16.99092     7.01   0.000     85.75158    152.3547
             time3 |   -134.343    27.3444    -4.91   0.000     -187.937   -80.74891
             _cons |   -4.28161   .6984306    -6.13   0.000    -5.650509   -2.912712
------------------------------------------------------------------------------------

.                         est store weeksD1

.                 * 2.3.1 logit Sample with xpers not missing *
.                 logit mzinit persrat_2014_1_lag milrat_2014_1_lag persxmil_lag $cvar
> 1 ///
>                         if democracy_1_lag==0 & xpers~=. & persrat_2014_1_lag~=.,  r
> obust cluster(dirdyadid)

Iteration 0:   log pseudolikelihood = -5390.6497  
Iteration 1:   log pseudolikelihood =  -4735.564  
Iteration 2:   log pseudolikelihood = -4512.3572  
Iteration 3:   log pseudolikelihood = -4162.9868  
Iteration 4:   log pseudolikelihood = -3533.9258  
Iteration 5:   log pseudolikelihood = -3492.4157  
Iteration 6:   log pseudolikelihood = -3490.3952  
Iteration 7:   log pseudolikelihood = -3490.3868  
Iteration 8:   log pseudolikelihood = -3490.3868  

Logistic regression                             Number of obs     =    318,672
                                                Wald chi2(19)     =    2017.42
                                                Prob > chi2       =     0.0000
Log pseudolikelihood = -3490.3868               Pseudo R2         =     0.3525

                               (Std. Err. adjusted for 13,295 clusters in dirdyadid)
------------------------------------------------------------------------------------
                   |               Robust
            mzinit |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------------+----------------------------------------------------------------
persrat_2014_1_lag |   1.087808   .2190897     4.97   0.000     .6584005    1.517216
 milrat_2014_1_lag |   1.269986    .261219     4.86   0.000     .7580064    1.781966
      persxmil_lag |  -.9376066    .297472    -3.15   0.002    -1.520641   -.3545721
   democracy_2_lag |   .5369526   .1481592     3.62   0.000     .2465659    .8273393
         cap_1_lag |   12.64225   5.573018     2.27   0.023     1.719333    23.56516
         cap_2_lag |   3.372097   1.636414     2.06   0.039     .1647841    6.579411
     initshare_lag |  -.0177687   .1960013    -0.09   0.928    -.4019242    .3663869
     dependlow_lag |   14.69517   4.349806     3.38   0.001     6.169706    23.22063
        majmaj_lag |   .6238384   .9173097     0.68   0.496    -1.174056    2.421732
        minmaj_lag |   1.311963   .2629958     4.99   0.000     .7965003    1.827425
        majmin_lag |  -.2495138   .7345649    -0.34   0.734    -1.689235    1.190207
     contigdum_lag |   2.132573   .6221666     3.43   0.001     .9131485    3.351997
       logdist_lag |  -.1773146   .0747203    -2.37   0.018    -.3237638   -.0308655
      s_wt_glo_lag |  -.2713378    .271162    -1.00   0.317    -.8028056      .26013
      s_lead_1_lag |  -.3344291   .3039166    -1.10   0.271    -.9300946    .2612365
      s_lead_2_lag |   .7979954   .2313742     3.45   0.001     .3445102    1.251481
              time |  -34.78834   3.076136   -11.31   0.000    -40.81746   -28.75923
             time2 |   121.2822    17.8415     6.80   0.000     86.31349    156.2509
             time3 |  -140.2512   29.15039    -4.81   0.000     -197.385   -83.11751
             _cons |  -4.468895   .7011936    -6.37   0.000    -5.843209   -3.094581
------------------------------------------------------------------------------------

.                         est store weeksD2       

.                 * 2.3.1 logit Sample with xpers not missing & xpers instead of persr
> at *
.                 logit mzinit xpers milrat_2014_1_lag xpersx $cvar1 ///
>                         if democracy_1_lag==0 & xpers~=. & persrat_2014_1_lag~=.,  r
> obust cluster(dirdyadid)

Iteration 0:   log pseudolikelihood = -5390.6497  
Iteration 1:   log pseudolikelihood = -4734.5597  
Iteration 2:   log pseudolikelihood = -4511.2121  
Iteration 3:   log pseudolikelihood =  -4160.391  
Iteration 4:   log pseudolikelihood = -3534.9674  
Iteration 5:   log pseudolikelihood = -3493.8057  
Iteration 6:   log pseudolikelihood = -3492.1125  
Iteration 7:   log pseudolikelihood = -3492.1061  
Iteration 8:   log pseudolikelihood = -3492.1061  

Logistic regression                             Number of obs     =    318,672
                                                Wald chi2(19)     =    2037.92
                                                Prob > chi2       =     0.0000
Log pseudolikelihood = -3492.1061               Pseudo R2         =     0.3522

                              (Std. Err. adjusted for 13,295 clusters in dirdyadid)
-----------------------------------------------------------------------------------
                  |               Robust
           mzinit |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
------------------+----------------------------------------------------------------
            xpers |   1.274984   .2494698     5.11   0.000     .7860318    1.763935
milrat_2014_1_lag |   1.139453   .2409251     4.73   0.000     .6672487    1.611658
    xpersxmil_lag |  -.9509929   .3888213    -2.45   0.014    -1.713069   -.1889171
  democracy_2_lag |   .5596719   .1501707     3.73   0.000     .2653426    .8540011
        cap_1_lag |   7.218613   5.641461     1.28   0.201    -3.838448    18.27567
        cap_2_lag |   3.530621   1.638971     2.15   0.031     .3182968    6.742944
    initshare_lag |   -.079406   .1914618    -0.41   0.678    -.4546643    .2958524
    dependlow_lag |   12.36972   4.573415     2.70   0.007     3.405989    21.33345
       majmaj_lag |   1.429116    .946355     1.51   0.131    -.4257053    3.283938
       minmaj_lag |    1.28398   .2595772     4.95   0.000     .7752177    1.792742
       majmin_lag |   .5949795   .7356503     0.81   0.419    -.8468687    2.036828
    contigdum_lag |   2.131862   .5994462     3.56   0.000     .9569692    3.306755
      logdist_lag |    -.17508   .0719554    -2.43   0.015      -.31611     -.03405
     s_wt_glo_lag |  -.2416747   .2641321    -0.91   0.360    -.7593641    .2760146
     s_lead_1_lag |  -.2796516   .2944407    -0.95   0.342    -.8567448    .2974416
     s_lead_2_lag |   .7849396   .2285762     3.43   0.001     .3369386    1.232941
             time |  -34.29845   3.094686   -11.08   0.000    -40.36393   -28.23298
            time2 |   117.4539   17.91753     6.56   0.000     82.33622    152.5716
            time3 |  -134.6144    29.2498    -4.60   0.000    -191.9429    -77.2858
            _cons |  -4.398767   .6896852    -6.38   0.000    -5.750525   -3.047009
-----------------------------------------------------------------------------------

.                         est store weeksD3       

.                 * 2.3.2 FE logit *
.                 xtlogit mzinit persrat_2014_1_lag milrat_2014_1_lag persxmil_lag dem
> ocracy_2_lag ///
>                         cap_1_lag cap_2_lag initshare_lag dependlow_lag  s_wt_glo_la
> g s_lead_1_lag s_lead_2_lag ///
>                         time time2 time3 if democracy_1_lag==0, fe
note: multiple positive outcomes within groups encountered.
note: 13,087 groups (312,993 obs) dropped because of all positive or
      all negative outcomes.

Iteration 0:   log likelihood = -2076.7237  
Iteration 1:   log likelihood =  -2009.967  
Iteration 2:   log likelihood = -2009.2811  
Iteration 3:   log likelihood = -2009.2807  
Iteration 4:   log likelihood = -2009.2807  

Conditional fixed-effects logistic regression   Number of obs     =     10,790
Group variable: dirdyadid                       Number of groups  =        312

                                                Obs per group:
                                                              min =          3
                                                              avg =       34.6
                                                              max =         50

                                                LR chi2(14)       =     105.98
Log likelihood  = -2009.2807                    Prob > chi2       =     0.0000

------------------------------------------------------------------------------------
            mzinit |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------------+----------------------------------------------------------------
persrat_2014_1_lag |   .9401397   .2984796     3.15   0.002     .3551304    1.525149
 milrat_2014_1_lag |   .8470738   .3998653     2.12   0.034     .0633522    1.630795
      persxmil_lag |  -1.127156   .5033207    -2.24   0.025    -2.113647   -.1406657
   democracy_2_lag |  -.1703045   .2108576    -0.81   0.419    -.5835778    .2429689
         cap_1_lag |   4.945229   8.656308     0.57   0.568    -12.02082    21.91128
         cap_2_lag |   5.596604   2.513071     2.23   0.026     .6710747    10.52213
     initshare_lag |  -1.596316   .9406986    -1.70   0.090    -3.440051    .2474195
     dependlow_lag |   21.65063   13.74672     1.57   0.115    -5.292447    48.59371
      s_wt_glo_lag |  -.3148938   .4176289    -0.75   0.451    -1.133431    .5036437
      s_lead_1_lag |  -1.491617   .4678573    -3.19   0.001      -2.4086    -.574633
      s_lead_2_lag |   .1177667   .5099812     0.23   0.817    -.8817781    1.117311
              time |  -12.20957   2.653658    -4.60   0.000    -17.41065     -7.0085
             time2 |   49.32776   16.26381     3.03   0.002     17.45128    81.20423
             time3 |   -34.0782   26.38178    -1.29   0.196    -85.78554    17.62914
------------------------------------------------------------------------------------

.                         est store weeksD4       

.                 * 2.3.2 FE logit reduced sample with xpers not missing *
.                 xtlogit mzinit persrat_2014_1_lag milrat_2014_1_lag persxmil_lag $cv
> ar2 ///
>                         if democracy_1_lag==0 & xpers~=. & persrat_2014_1_lag~=., fe
note: multiple positive outcomes within groups encountered.
note: 12,986 groups (308,082 obs) dropped because of all positive or
      all negative outcomes.

Iteration 0:   log likelihood = -2050.8381  
Iteration 1:   log likelihood = -1983.9651  
Iteration 2:   log likelihood = -1983.1571  
Iteration 3:   log likelihood = -1983.1565  
Iteration 4:   log likelihood = -1983.1565  

Conditional fixed-effects logistic regression   Number of obs     =     10,590
Group variable: dirdyadid                       Number of groups  =        309

                                                Obs per group:
                                                              min =          3
                                                              avg =       34.3
                                                              max =         50

                                                LR chi2(14)       =     102.98
Log likelihood  = -1983.1565                    Prob > chi2       =     0.0000

------------------------------------------------------------------------------------
            mzinit |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------------+----------------------------------------------------------------
persrat_2014_1_lag |   1.052228   .3011503     3.49   0.000     .4619838    1.642471
 milrat_2014_1_lag |   1.017505   .4121758     2.47   0.014     .2096548    1.825354
      persxmil_lag |  -1.283041   .5104642    -2.51   0.012    -2.283533     -.28255
   democracy_2_lag |  -.1837848   .2127382    -0.86   0.388    -.6007441    .2331744
         cap_1_lag |   8.634093   9.819114     0.88   0.379    -10.61102     27.8792
         cap_2_lag |   5.429327   2.527693     2.15   0.032     .4751396    10.38351
     initshare_lag |   -1.82641   .9478854    -1.93   0.054    -3.684232     .031411
     dependlow_lag |   21.53996     13.822     1.56   0.119    -5.550656    48.63058
      s_wt_glo_lag |   -.238614   .4275823    -0.56   0.577     -1.07666    .5994318
      s_lead_1_lag |  -1.580597   .4887914    -3.23   0.001    -2.538611   -.6225836
      s_lead_2_lag |    .217533   .5181421     0.42   0.675    -.7980068    1.233073
              time |  -12.55072   2.686448    -4.67   0.000    -17.81607   -7.285384
             time2 |   53.41003   16.56705     3.22   0.001     20.93921    85.88084
             time3 |  -42.25344   27.06489    -1.56   0.118    -95.29966    10.79278
------------------------------------------------------------------------------------

.                         est store weeksD5

.                 * 2.3.2 FE logit reduced sample with xpers not missing & xpers inste
> ad of persrat *
.                 xtlogit mzinit xpers milrat_2014_1_lag xpersx $cvar2 if democracy_1_
> lag==0 & ///
>                         xpers~=. & persrat_2014_1_lag~=., fe
note: multiple positive outcomes within groups encountered.
note: 12,986 groups (308,082 obs) dropped because of all positive or
      all negative outcomes.

Iteration 0:   log likelihood = -2053.3003  
Iteration 1:   log likelihood = -1989.6714  
Iteration 2:   log likelihood = -1988.9122  
Iteration 3:   log likelihood = -1988.9116  
Iteration 4:   log likelihood = -1988.9116  

Conditional fixed-effects logistic regression   Number of obs     =     10,590
Group variable: dirdyadid                       Number of groups  =        309

                                                Obs per group:
                                                              min =          3
                                                              avg =       34.3
                                                              max =         50

                                                LR chi2(14)       =      91.47
Log likelihood  = -1988.9116                    Prob > chi2       =     0.0000

-----------------------------------------------------------------------------------
           mzinit |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
------------------+----------------------------------------------------------------
            xpers |   .2083818   .3093716     0.67   0.501    -.3979754    .8147389
milrat_2014_1_lag |   .2833891   .3703921     0.77   0.444    -.4425662    1.009344
    xpersxmil_lag |  -.2801621   .5449931    -0.51   0.607    -1.348329    .7880048
  democracy_2_lag |  -.2054977   .2120392    -0.97   0.332    -.6210869    .2100915
        cap_1_lag |   3.228457   9.451819     0.34   0.733    -15.29677    21.75368
        cap_2_lag |   5.897388   2.485847     2.37   0.018     1.025217    10.76956
    initshare_lag |   -1.67722   .9509601    -1.76   0.078    -3.541067    .1866276
    dependlow_lag |   21.33828   13.29338     1.61   0.108    -4.716264    47.39283
     s_wt_glo_lag |  -.4306509   .4225506    -1.02   0.308    -1.258835    .3975331
     s_lead_1_lag |  -1.469409   .4801939    -3.06   0.002    -2.410572   -.5282467
     s_lead_2_lag |  -.0546908   .5095819    -0.11   0.915    -1.053453    .9440713
             time |  -12.01957   2.671775    -4.50   0.000    -17.25615   -6.782987
            time2 |   47.58253   16.42999     2.90   0.004     15.38034    79.78472
            time3 |  -32.67238   26.88181    -1.22   0.224    -85.35976    20.01501
-----------------------------------------------------------------------------------

.                         est store weeksD6

.                         
.                 * Figure D: add new regimes *
.                   label var xpers  "G-pers"

.                   label var milrat_2014_1_lag  "W-mil"

.                   label var persrat_2014_1_lag  "W-pers"

.                   label var persxmil_lag  `""W-pers" "x    " "W-mil ""'

.                   label var xpersx  `""G-pers" "x    " "W-mil ""'

.                 coefplot (weeksD1, msymbol(d)) (weeksD2, msymbol(t)) (weeksD3, msymb
> ol(s)), title("Logit", size(medium)) ///
>                         scheme(plottig) drop(_cons democracy_2_lag  cap_1_lag cap_2_
> lag initshare_lag dependlow_lag majmaj_lag ///
>                          minmaj_lag majmin_lag contigdum_lag logdist_lag s_wt_glo_la
> g s_lead_1_lag s_lead_2_lag time time2 time3) xline(0) ///
>                         grid(glcolor(gs15)) mfcolor(white) xlabel(-2 (.5) 2)  levels
> (95 90) xtitle("Coefficient estimate", height(6)) ///
>                         legend(lab(3 "Original") lab(6 "Orignal-adjusted sample") la
> b(9 "G-pers-adjusted sample")size(vsmall) pos(6) ring(1.5) col(3)) ///
>                         ysize(1) xsize(1.5) saving(r1, replace)                 
(file r1.gph saved)

.                 coefplot (weeksD4, msymbol(d)) (weeksD5, msymbol(t)) (weeksD6, msymb
> ol(s)) , title("FE-Logit", size(medium)) ///
>                         scheme(plottig) drop(_cons democracy_2_lag  cap_1_lag cap_2_
> lag initshare_lag dependlow_lag majmaj_lag ///
>                          minmaj_lag majmin_lag contigdum_lag logdist_lag s_wt_glo_la
> g s_lead_1_lag s_lead_2_lag time time2 time3) xline(0) ///
>                         grid(glcolor(gs15)) mfcolor(white) xlabel(-2 (.5) 2)  levels
> (95 90) xtitle("Coefficient estimate", height(6)) ///
>                         legend(lab(3 "Original") lab(6 "Orignal-adjusted sample") la
> b(9 "G-pers-adjusted sample")size(vsmall) pos(6) ring(1.5) col(3)) ///
>                         ysize(1) xsize(1.5) saving(r2, replace)         
(file r2.gph saved)

.                 gr combine r1.gph r2.gph, iscale(.8) xsize(6)  

.                 graph export "$dir/golden/MID-D4.pdf", as(pdf) replace
(file /Users/lee/Dropbox/Datavers/golden/MID-D4.pdf written in PDF format)

.                 
.                 est restore weeksD6
(results weeksD6 are active now)

.                 tab milrat_2014_1_lag if e(sample)

      W-mil |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |      3,046       28.76       28.76
         .2 |        426        4.02       32.79
        .25 |      1,872       17.68       50.46
   .3333333 |      1,215       11.47       61.94
         .4 |        129        1.22       63.15
         .5 |         89        0.84       63.99
         .6 |        843        7.96       71.95
   .6666667 |        327        3.09       75.04
        .75 |        381        3.60       78.64
         .8 |        923        8.72       87.36
          1 |      1,339       12.64      100.00
------------+-----------------------------------
      Total |     10,590      100.00

.                 margins, dydx(xpers) at(milrat_2014_1_lag=(0(.05)1)) vsquish post

Average marginal effects                        Number of obs     =     10,590
Model VCE    : OIM

Expression   : Pr(mzinit|fixed effect is 0), predict(pu0)
dy/dx w.r.t. : xpers
1._at        : milrat_201~g    =           0
2._at        : milrat_201~g    =         .05
3._at        : milrat_201~g    =          .1
4._at        : milrat_201~g    =         .15
5._at        : milrat_201~g    =          .2
6._at        : milrat_201~g    =         .25
7._at        : milrat_201~g    =          .3
8._at        : milrat_201~g    =         .35
9._at        : milrat_201~g    =          .4
10._at       : milrat_201~g    =         .45
11._at       : milrat_201~g    =          .5
12._at       : milrat_201~g    =         .55
13._at       : milrat_201~g    =          .6
14._at       : milrat_201~g    =         .65
15._at       : milrat_201~g    =          .7
16._at       : milrat_201~g    =         .75
17._at       : milrat_201~g    =          .8
18._at       : milrat_201~g    =         .85
19._at       : milrat_201~g    =          .9
20._at       : milrat_201~g    =         .95
21._at       : milrat_201~g    =           1

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
xpers        |
         _at |
          1  |   .0321826   .0486105     0.66   0.508    -.0630923    .1274574
          2  |   .0323683   .0490124     0.66   0.509    -.0636942    .1284308
          3  |   .0325536   .0494134     0.66   0.510    -.0642949    .1294021
          4  |   .0327383   .0498135     0.66   0.511    -.0648943    .1303709
          5  |   .0329225   .0502124     0.66   0.512     -.065492     .131337
          6  |   .0331061   .0506101     0.65   0.513    -.0660879    .1323001
          7  |   .0332891   .0510064     0.65   0.514    -.0666817    .1332599
          8  |   .0334714   .0514012     0.65   0.515    -.0672731     .134216
          9  |   .0336531   .0517944     0.65   0.516     -.067862    .1351683
         10  |   .0338341   .0521858     0.65   0.517    -.0684481    .1361163
         11  |   .0340144   .0525752     0.65   0.518    -.0690311    .1370599
         12  |   .0341939   .0529626     0.65   0.519    -.0696108    .1379986
         13  |   .0343726   .0533477     0.64   0.519     -.070187    .1389322
         14  |   .0345505   .0537305     0.64   0.520    -.0707594    .1398605
         15  |   .0347276   .0541109     0.64   0.521    -.0713277     .140783
         16  |   .0349038   .0544886     0.64   0.522    -.0718918    .1416995
         17  |   .0350791   .0548635     0.64   0.523    -.0724514    .1426097
         18  |   .0352535   .0552356     0.64   0.523    -.0730062    .1435132
         19  |    .035427   .0556045     0.64   0.524    -.0735559    .1444099
         20  |   .0355994   .0559703     0.64   0.525    -.0741004    .1452993
         21  |   .0357708   .0563328     0.63   0.525    -.0746394    .1461811
------------------------------------------------------------------------------

.                 matrix at=e(at)

.                 matrix at=at[1...,"milrat_2014_1_lag"]

.                 matrix list at

at[21,1]
        milrat_201~g
 1._at             0
 2._at           .05
 3._at            .1
 4._at           .15
 5._at            .2
 6._at           .25
 7._at            .3
 8._at           .35
 9._at            .4
10._at           .45
11._at            .5
12._at           .55
13._at            .6
14._at           .65
15._at            .7
16._at           .75
17._at            .8
18._at           .85
19._at            .9
20._at           .95
21._at             1

.                 parmest, norestore

.                 svmat at

.                 twoway (line min95 at1, lpattern(dash) lcol(blue)) (line estimate at
> 1,lcol(blue)) ///
>                         (line max95 at1,lcol(blue) lpattern(dash)), legend(order (1 
> "Upper 95% c.i." 3 "Lower 95% c.i.")) ///
>                         yline(0,lcol(gs5))  xtitle(Score on militarism index) title(
> G-personalism) legend(pos(6) col(2)) ///
>                         ytitle(Marginal effect of personalism) saving(r2.gph,replace
> ) scheme(plottig)ylab(-.1(.1).4)
(file r2.gph saved)

.                         
.                 use "$dir/temp-Weeks.dta",clear 

.                 xtset dirdyadid year
       panel variable:  dirdyadid (unbalanced)
        time variable:  year, 1945 to 2000, but with gaps
                delta:  1 unit

.                 qui xtlogit mzinit persrat_2014_1_lag milrat_2014_1_lag persxmil_lag
>  democracy_2_lag ///
>                         cap_1_lag cap_2_lag initshare_lag dependlow_lag  s_wt_glo_la
> g s_lead_1_lag s_lead_2_lag ///
>                         time time2 time3 if democracy_1_lag==0, fe

.                 est store weeksD4       

.                 tab milrat_2014_1_lag if e(sample)

milrat_2014 |
     _1_lag |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |      3,094       28.67       28.67
         .2 |        429        3.98       32.65
        .25 |      1,907       17.67       50.32
   .3333333 |      1,222       11.33       61.65
         .4 |        130        1.20       62.85
         .5 |        106        0.98       63.84
         .6 |        851        7.89       71.72
   .6666667 |        336        3.11       74.84
        .75 |        394        3.65       78.49
         .8 |        932        8.64       87.13
          1 |      1,389       12.87      100.00
------------+-----------------------------------
      Total |     10,790      100.00

.                 margins, dydx(persrat_2014_1_lag) at(milrat_2014_1_lag=(0(.05)1)) vs
> quish post

Average marginal effects                        Number of obs     =     10,790
Model VCE    : OIM

Expression   : Pr(mzinit|fixed effect is 0), predict(pu0)
dy/dx w.r.t. : persrat_2014_1_lag
1._at        : milrat_201~g    =           0
2._at        : milrat_201~g    =         .05
3._at        : milrat_201~g    =          .1
4._at        : milrat_201~g    =         .15
5._at        : milrat_201~g    =          .2
6._at        : milrat_201~g    =         .25
7._at        : milrat_201~g    =          .3
8._at        : milrat_201~g    =         .35
9._at        : milrat_201~g    =          .4
10._at       : milrat_201~g    =         .45
11._at       : milrat_201~g    =          .5
12._at       : milrat_201~g    =         .55
13._at       : milrat_201~g    =          .6
14._at       : milrat_201~g    =         .65
15._at       : milrat_201~g    =          .7
16._at       : milrat_201~g    =         .75
17._at       : milrat_201~g    =          .8
18._at       : milrat_201~g    =         .85
19._at       : milrat_201~g    =          .9
20._at       : milrat_201~g    =         .95
21._at       : milrat_201~g    =           1

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
persrat_20~g |
         _at |
          1  |   .1676319   .0677438     2.47   0.013     .0348565    .3004073
          2  |   .1696451     .06821     2.49   0.013     .0359559    .3033343
          3  |   .1715994    .068645     2.50   0.012     .0370577    .3061412
          4  |   .1734914   .0690127     2.51   0.012      .038229    .3087539
          5  |   .1753178   .0693106     2.53   0.011     .0394716     .311164
          6  |   .1770753   .0695369     2.55   0.011     .0407856    .3133651
          7  |   .1787607   .0696876     2.57   0.010     .0421755    .3153458
          8  |   .1803708   .0697707     2.59   0.010     .0436227     .317119
          9  |   .1819027   .0697767     2.61   0.009     .0451429    .3186625
         10  |   .1833534   .0697033     2.63   0.009     .0467374    .3199693
         11  |     .18472   .0695488     2.66   0.008     .0484069    .3210331
         12  |       .186   .0693119     2.68   0.007     .0501512    .3218488
         13  |   .1871906   .0689918     2.71   0.007     .0519691    .3224121
         14  |   .1882896   .0685883     2.75   0.006     .0538589    .3227203
         15  |   .1892946   .0681018     2.78   0.005     .0558176    .3227716
         16  |   .1902035    .067533     2.82   0.005     .0578412    .3225658
         17  |   .1910144   .0668837     2.86   0.004     .0599247    .3221041
         18  |   .1917256   .0661561     2.90   0.004      .062062    .3213892
         19  |   .1923354   .0653532     2.94   0.003     .0642455    .3204253
         20  |   .1928425   .0644788     2.99   0.003     .0664664    .3192186
         21  |   .1932456   .0635375     3.04   0.002     .0687145    .3177768
------------------------------------------------------------------------------

.                 matrix at=e(at)

.                 matrix at=at[1...,"milrat_2014_1_lag"]

.                 matrix list at

at[21,1]
        milrat_201~g
 1._at             0
 2._at           .05
 3._at            .1
 4._at           .15
 5._at            .2
 6._at           .25
 7._at            .3
 8._at           .35
 9._at            .4
10._at           .45
11._at            .5
12._at           .55
13._at            .6
14._at           .65
15._at            .7
16._at           .75
17._at            .8
18._at           .85
19._at            .9
20._at           .95
21._at             1

.                 parmest, norestore

.                 svmat at

.                 twoway (line min95 at1, lpattern(dash) lcol(blue)) (line estimate at
> 1,lcol(blue)) ///
>                         (line max95 at1,lcol(blue) lpattern(dash)), legend(order (1 
> "Upper 95% c.i." 3 "Lower 95% c.i.")) ///
>                         yline(0,lcol(gs5))  xtitle(Score on militarism index) title(
> W-personalism) legend(pos(6) col(2)) ///
>                         ytitle(Marginal effect of personalism) saving(r1.gph,replace
> ) scheme(plottig)ylab(-.1(.1).4)
(file r1.gph saved)

.                 
.                 erase r1.gph

.                 erase r2.gph

.                 erase r3.gph

.         
.         ****************************************************************************
> ****************************************************
.         **** Look at how uncertainty in Personalism latent estimates influence the W
> eeks' result, b/c logit, estimates could differ ****
.         ****************************************************************************
> ****************************************************
.         use temp-Weeks, clear

.         xtlogit mzinit xpers milrat_2014_1_lag xpersx $cvar2 if democracy_1_lag==0 &
>  ///
>                 newregime_1_lag!=1 & xpers~=. & persrat_2014_1_lag~=., fe
note: multiple positive outcomes within groups encountered.
note: 12,615 groups (259,498 obs) dropped because of all positive or
      all negative outcomes.

Iteration 0:   log likelihood = -1660.2744  
Iteration 1:   log likelihood = -1608.0297  
Iteration 2:   log likelihood = -1607.7313  
Iteration 3:   log likelihood = -1607.7313  

Conditional fixed-effects logistic regression   Number of obs     =      8,158
Group variable: dirdyadid                       Number of groups  =        264

                                                Obs per group:
                                                              min =          4
                                                              avg =       30.9
                                                              max =         49

                                                LR chi2(14)       =      97.87
Log likelihood  = -1607.7313                    Prob > chi2       =     0.0000

-----------------------------------------------------------------------------------
           mzinit |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
------------------+----------------------------------------------------------------
            xpers |   .3887555   .3355217     1.16   0.247     -.268855    1.046366
milrat_2014_1_lag |   .6544375    .466575     1.40   0.161    -.2600326    1.568908
    xpersxmil_lag |  -.1028453   .6483967    -0.16   0.874     -1.37368    1.167989
  democracy_2_lag |  -.1645793   .2384587    -0.69   0.490    -.6319498    .3027912
        cap_1_lag |  -5.352864   10.24839    -0.52   0.601    -25.43934    14.73361
        cap_2_lag |   9.375053   2.875939     3.26   0.001     3.738316    15.01179
    initshare_lag |   .3741399   1.056835     0.35   0.723    -1.697219    2.445499
    dependlow_lag |   26.67775   16.32733     1.63   0.102    -5.323223    58.67872
     s_wt_glo_lag |  -.1672913   .5024775    -0.33   0.739    -1.152129    .8175466
     s_lead_1_lag |  -1.511541   .5661626    -2.67   0.008    -2.621199   -.4018824
     s_lead_2_lag |   .0329554   .5576539     0.06   0.953    -1.060026    1.125937
             time |  -7.693298   3.088699    -2.49   0.013    -13.74704   -1.639559
            time2 |   13.45609   19.83976     0.68   0.498    -25.42913     52.3413
            time3 |   39.56376   33.93445     1.17   0.244    -26.94653    106.0741
-----------------------------------------------------------------------------------

.         keep if e(sample)==1
(1,071,448 observations deleted)

.         set seed 987245

.         gen latentperssim = rnormal(irtpers8,se_irtpers8)

.         gen latentMxPsim = latentperssim*milrat_2014_1_lag

.         global cvar2 = "democracy_2_lag cap_1_lag cap_2_lag initshare_lag dependlow_
> lag  s_wt_glo_lag s_lead_1_lag s_lead_2_lag time time2 time3 "

. 
.         capture program drop latentsim

.         program define latentsim
  1.                 drop latentperssim  latentMxPsim
  2.                 gen latentperssim = rnormal(irtpers8,se_irtpers8)
  3.                 gen latentMxPsim = latentperssim*milrat_2014_1_lag
  4.                 qui:xtlogit mzinit latentperssim milrat_2014_1_lag  latentMxPsim 
>  $cvar2, fe
  5.         end

. 
.         *simulate 1000 times and store coefficients
.         simulate _b _se, rep(1000):latentsim

      command:  latentsim

Simulations (1000)
----+--- 1 ---+--- 2 ---+--- 3 ---+--- 4 ---+--- 5 
..................................................    50
..................................................   100
..................................................   150
..................................................   200
..................................................   250
..................................................   300
..................................................   350
..................................................   400
..................................................   450
..................................................   500
..................................................   550
..................................................   600
..................................................   650
..................................................   700
..................................................   750
..................................................   800
..................................................   850
..................................................   900
..................................................   950
..................................................  1000

. 
.         *Save result to a dta file
.         save simbeta.dta,replace
(note: file simbeta.dta not found)
file simbeta.dta saved

.          
.          * calculate sims means and (between & within) standard errors from sims sto
> red as variable values *
.                 gen varname =""
(1,000 missing values generated)

.                 gen meanbeta=.
(1,000 missing values generated)

.                 gen varbeta=.
(1,000 missing values generated)

.                 local var ="_b_latentMxPsim _b_milrat_2014_1_lag  _b_latentperssim"

.                 local i =1

.                 gen n =_n

.                 foreach v of local var {
  2.                         replace varname = "`v'" if n==`i'
  3.                         qui sum mzinit`v'
  4.                         replace meanbeta = r(mean) if n==`i'
  5.                         replace varbeta = r(Var) if n==`i'
  6.                         local i = `i'+1
  7.                 }
variable varname was str1 now str15
(1 real change made)
(1 real change made)
(1 real change made)
variable varname was str15 now str20
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)

.                 gen meanse=.
(1,000 missing values generated)

.                 local var ="_b_latentMxPsim _b_milrat_2014_1_lag  _b_latentperssim"

.                 local i =1

.                 foreach v of local var {
  2.                         qui sum mzinit`v'
  3.                         replace meanse = r(mean) if n==`i'
  4.                         local i = `i'+1
  5.                 }
(1 real change made)
(1 real change made)
(1 real change made)

.                 
.         * calculate overall standard errors
.         qui count

.         gen simse = sqrt(meanse^2+varbeta*(1+1/r(N)))
(997 missing values generated)

.         /* 
>                 Rubin (1987)
>                 Estimate of beta is the mean of the betas from each estimated beta 
>                 Estimate of variance is: Vb + Vw + Vb/m
>                         where Vb is the between variance, Vw is the mean variance, a
> nd Vb/m is the sampling variance:
>                                 Vb is the sum of the squared deviations from the mea
> n of the estimated betas
>                                 Vw is the mean of the sampling variances (SE) from e
> ach of the m simluated variances
>         */ 
.         * generate 95% CI
.         gen lo = meanbeta - 1.96*simse
(997 missing values generated)

.         gen hi = meanbeta + 1.96*simse
(997 missing values generated)

.         gen est = round(meanbeta, 0.001)
(997 missing values generated)

. 
.         *Plot coefficients with uncertainty
.         twoway (rspike lo hi n if n<4, hori col(black)lwidth(medium)) (scatter n mea
> nbeta if n<4,col(black) msymbol(d) msize(medium) ///
>                         xtitle("Coefficient estimate", height(6)size(small)) ytitle(
> "") legend(off) ysize(1) xsize(1.5) xline(0,lpat(dash)) mfcolor(white) ///
>                         title("Modeling uncertainty in the latent personalism  measu
> re",  size(medium) ) ///
>                         ylab(1 "G-pers X W-mil" 2 "W-mil"  3  "G-pers") ylabel(, ang
> le(0)))  ///
>                         (scatter n meanbeta if n<4,col(red)msize(vsmall)msymbol(plus
> )mlabel(est) mlabposition(2) )

.         graph export "$dir/golden/MID-uncertainty.pdf", as(pdf) replace
(file /Users/lee/Dropbox/Datavers/golden/MID-uncertainty.pdf written in PDF format)

.                 
.                         
.                 
.         ******************************************************************
.         ********* Appendix E: Replicate regime failure analysis **********
.         ******************************************************************
.         use "$dir/temp.dta",clear

.  
.         ** Different post-collapse transitions **
.                 gen ged_dem =  gwf_fail_subsregime

.                 recode ged_dem (3=0) (2=0) (4=0)
(ged_dem: 122 changes made)

.                 replace ged_dem=1 if (cowcode==265 & year==1990 )   /*East Germany *
> /
(1 real change made)

.                 tab ged_dem gwf_fail_subs

           |  Regime case failure, by subsequent regime
           |                    type
   ged_dem |         0          1          2          3 |     Total
-----------+--------------------------------------------+----------
         0 |     4,368          0        111         10 |     4,489 
         1 |         0        101          0          1 |       102 
-----------+--------------------------------------------+----------
     Total |     4,368        101        111         11 |     4,591 


.                 gen ged_dict = gwf_fail_subs

.                 recode ged_dict (3=1)(1=0) (2=1) (4=0)
(ged_dict: 223 changes made)

.          
.         ** Regime Duration Time**
.                 gen ged_time = gwf_case_duration

.                 gen ged_time2 = ged_time^2

.                 gen ged_time3 = ged_time^3

.                 
.         ** Decade dummies **
.                 gen decade = year<1960

.                 replace decade = 2 if year>=1960 & year<1970
(744 real changes made)

.                 replace decade = 3 if year>=1970 & year<1980
(937 real changes made)

.                 replace decade = 4 if year>=1980 & year<1990
(911 real changes made)

.                 replace decade = 5 if year>=1990 & year<2000
(721 real changes made)

.                 
.         ** Control Variables**
.                 tsset cow year
       panel variable:  cowcode (unbalanced)
        time variable:  year, 1946 to 2010, but with gaps
                delta:  1 unit

.                 gen lgdp  = mad_lgdppc 
(219 missing values generated)

.                 replace grow = grow*10
(4,397 real changes made)

.                 gen prevdem = gwf_prior=="democracy" | gwf_prior == "provisional" if
>  gwf_fail~=.

.                 
.         ** Control variable sets **
.                 global time  = "ged_time ged_time2 ged_time3 i.decade"

.                 global covar = "prevdem lgdp grow intwar civwar"

.                 
.         ** Label vars **
.                  label var xirtpers8 "G-pers"

.                  label var Personal `""Personalist" "regime   ""'

.                  label var Military `""Military" "regime""'

.                          
.                  xi:xtreg gwf_case_fail $time $covar Military Personal if Monarchy==
> 0,vce(cluster cow)
i.decade          _Idecade_0-5        (naturally coded; _Idecade_0 omitted)

Random-effects GLS regression                   Number of obs     =      3,726
Group variable: cowcode                         Number of groups  =        105

R-sq:                                           Obs per group:
     within  = 0.0151                                         min =          7
     between = 0.2665                                         avg =       35.5
     overall = 0.0334                                         max =         61

                                                Wald chi2(15)     =     102.33
corr(u_i, X)   = 0 (assumed)                    Prob > chi2       =     0.0000

                              (Std. Err. adjusted for 105 clusters in cowcode)
------------------------------------------------------------------------------
             |               Robust
gwf_case_f~l |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    ged_time |  -.0001308   .0015367    -0.09   0.932    -.0031427    .0028811
   ged_time2 |  -1.25e-06   .0000544    -0.02   0.982    -.0001078    .0001053
   ged_time3 |   1.77e-07   4.88e-07     0.36   0.717    -7.80e-07    1.13e-06
  _Idecade_1 |   .0204165    .016998     1.20   0.230    -.0128989     .053732
  _Idecade_2 |   .0164564   .0155875     1.06   0.291    -.0140946    .0470074
  _Idecade_3 |  -.0050067   .0121422    -0.41   0.680     -.028805    .0187916
  _Idecade_4 |  -.0052947   .0118614    -0.45   0.655    -.0285426    .0179532
  _Idecade_5 |   .0269931   .0135292     2.00   0.046     .0004764    .0535098
     prevdem |   .0175681   .0103123     1.70   0.088    -.0026436    .0377799
        lgdp |  -.0051605   .0048247    -1.07   0.285    -.0146166    .0042957
        grow |  -.0028607   .0054241    -0.53   0.598    -.0134918    .0077704
      intwar |   .0154727   .0243001     0.64   0.524    -.0321546       .0631
      civwar |   .0356257   .0239589     1.49   0.137    -.0113329    .0825844
    Military |   .1033297   .0169513     6.10   0.000     .0701058    .1365536
 Personalist |   .0333404   .0084602     3.94   0.000     .0167587    .0499222
       _cons |   .0527401   .0387227     1.36   0.173    -.0231549    .1286352
-------------+----------------------------------------------------------------
     sigma_u |          0
     sigma_e |  .21726972
         rho |          0   (fraction of variance due to u_i)
------------------------------------------------------------------------------

.                  est store g1

.                  xi:xtreg gwf_case_fail $time $covar Military Personal if Monarchy==
> 0,fe  vce(cluster cow)
i.decade          _Idecade_0-5        (naturally coded; _Idecade_0 omitted)

Fixed-effects (within) regression               Number of obs     =      3,726
Group variable: cowcode                         Number of groups  =        105

R-sq:                                           Obs per group:
     within  = 0.0256                                         min =          7
     between = 0.0177                                         avg =       35.5
     overall = 0.0161                                         max =         61

                                                F(15,104)         =       6.36
corr(u_i, Xb)  = -0.3574                        Prob > F          =     0.0000

                              (Std. Err. adjusted for 105 clusters in cowcode)
------------------------------------------------------------------------------
             |               Robust
gwf_case_f~l |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    ged_time |   .0039569   .0016852     2.35   0.021      .000615    .0072988
   ged_time2 |  -.0000653   .0000556    -1.17   0.243    -.0001755     .000045
   ged_time3 |   7.54e-07   4.74e-07     1.59   0.114    -1.85e-07    1.69e-06
  _Idecade_1 |   .0236346   .0342839     0.69   0.492    -.0443516    .0916209
  _Idecade_2 |   .0215354   .0296284     0.73   0.469    -.0372188    .0802896
  _Idecade_3 |  -.0078847   .0229932    -0.34   0.732     -.053481    .0377116
  _Idecade_4 |  -.0163665   .0195953    -0.84   0.406    -.0552248    .0224917
  _Idecade_5 |   .0233671   .0150188     1.56   0.123    -.0064159      .05315
     prevdem |   .0351496   .0367086     0.96   0.341     -.037645    .1079441
        lgdp |  -.0316794   .0200624    -1.58   0.117    -.0714638     .008105
        grow |   .0016915   .0059941     0.28   0.778     -.010195     .013578
      intwar |   .0480326    .032876     1.46   0.147    -.0171617    .1132268
      civwar |   .0645311   .0251991     2.56   0.012     .0145603    .1145018
    Military |   .1051133   .0317446     3.31   0.001     .0421625    .1680642
 Personalist |     .00973    .029063     0.33   0.738     -.047903    .0673629
       _cons |   .2027555   .1523857     1.33   0.186     -.099431     .504942
-------------+----------------------------------------------------------------
     sigma_u |  .07636706
     sigma_e |  .21726972
         rho |  .10995742   (fraction of variance due to u_i)
------------------------------------------------------------------------------

.                  est store g2

.                  xi:xtreg gwf_case_fail $time $covar Military xirtpers8 if Monarchy=
> =0,vce(cluster cow)
i.decade          _Idecade_0-5        (naturally coded; _Idecade_0 omitted)

Random-effects GLS regression                   Number of obs     =      3,726
Group variable: cowcode                         Number of groups  =        105

R-sq:                                           Obs per group:
     within  = 0.0167                                         min =          7
     between = 0.2492                                         avg =       35.5
     overall = 0.0311                                         max =         61

                                                Wald chi2(15)     =      76.17
corr(u_i, X)   = 0 (assumed)                    Prob > chi2       =     0.0000

                              (Std. Err. adjusted for 105 clusters in cowcode)
------------------------------------------------------------------------------
             |               Robust
gwf_case_f~l |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    ged_time |   .0000167   .0015772     0.01   0.992    -.0030747     .003108
   ged_time2 |  -.0000191   .0000559    -0.34   0.733    -.0001286    .0000905
   ged_time3 |   3.20e-07   4.99e-07     0.64   0.522    -6.59e-07    1.30e-06
  _Idecade_1 |    .005914   .0165924     0.36   0.722    -.0266065    .0384345
  _Idecade_2 |   .0016887   .0152932     0.11   0.912    -.0282854    .0316628
  _Idecade_3 |  -.0151171   .0127569    -1.19   0.236    -.0401202     .009886
  _Idecade_4 |  -.0123613   .0116166    -1.06   0.287    -.0351294    .0104068
  _Idecade_5 |   .0226773   .0135125     1.68   0.093    -.0038066    .0491613
     prevdem |   .0230056   .0106438     2.16   0.031     .0021442     .043867
        lgdp |  -.0079485   .0052359    -1.52   0.129    -.0182106    .0023136
        grow |  -.0035059    .005415    -0.65   0.517    -.0141191    .0071073
      intwar |   .0111877   .0263485     0.42   0.671    -.0404544    .0628298
      civwar |   .0336931   .0242356     1.39   0.164    -.0138079     .081194
    Military |   .0813206   .0164293     4.95   0.000     .0491197    .1135215
   xirtpers8 |  -.0295521   .0147206    -2.01   0.045     -.058404   -.0007002
       _cons |   .1105298   .0430735     2.57   0.010     .0261073    .1949523
-------------+----------------------------------------------------------------
     sigma_u |          0
     sigma_e |  .21682264
         rho |          0   (fraction of variance due to u_i)
------------------------------------------------------------------------------

.                  est store g3

.                  xi:xtreg gwf_case_fail $time $covar Military xirtpers8 if Monarchy=
> =0,fe vce(cluster cow)
i.decade          _Idecade_0-5        (naturally coded; _Idecade_0 omitted)

Fixed-effects (within) regression               Number of obs     =      3,726
Group variable: cowcode                         Number of groups  =        105

R-sq:                                           Obs per group:
     within  = 0.0296                                         min =          7
     between = 0.0172                                         avg =       35.5
     overall = 0.0153                                         max =         61

                                                F(15,104)         =       7.53
corr(u_i, Xb)  = -0.4327                        Prob > F          =     0.0000

                              (Std. Err. adjusted for 105 clusters in cowcode)
------------------------------------------------------------------------------
             |               Robust
gwf_case_f~l |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    ged_time |   .0055345   .0017468     3.17   0.002     .0020705    .0089985
   ged_time2 |  -.0001147    .000058    -1.98   0.051    -.0002298    3.93e-07
   ged_time3 |   1.10e-06   4.85e-07     2.27   0.025     1.38e-07    2.06e-06
  _Idecade_1 |   .0117079   .0301265     0.39   0.698    -.0480341      .07145
  _Idecade_2 |    .008341   .0248364     0.34   0.738    -.0409104    .0575925
  _Idecade_3 |   -.014885   .0204433    -0.73   0.468    -.0554249    .0256548
  _Idecade_4 |  -.0182463   .0176915    -1.03   0.305    -.0533291    .0168366
  _Idecade_5 |   .0224263    .014941     1.50   0.136    -.0072023    .0520549
     prevdem |    .042075   .0363747     1.16   0.250    -.0300575    .1142074
        lgdp |  -.0358568   .0193059    -1.86   0.066    -.0741411    .0024276
        grow |      .0015   .0058511     0.26   0.798    -.0101029    .0131029
      intwar |    .049075   .0341466     1.44   0.154    -.0186389    .1167889
      civwar |   .0607957   .0250727     2.42   0.017     .0110756    .1105158
    Military |   .0864139   .0288224     3.00   0.003      .029258    .1435698
   xirtpers8 |   -.078151    .023893    -3.27   0.001    -.1255318   -.0307702
       _cons |   .2687083      .1447     1.86   0.066    -.0182373    .5556538
-------------+----------------------------------------------------------------
     sigma_u |  .07846887
     sigma_e |  .21682264
         rho |  .11580647   (fraction of variance due to u_i)
------------------------------------------------------------------------------

.                  est store g4

.                  coefplot (g1, msymbol(d))  (g3, msymbol(s)) , title("Autocratic reg
> ime collapse", size(medium)) ///
>                                 scheme(plottig) drop(_cons _Idecade_* $time $covar) 
> xline(0) ///
>                                 grid(glcolor(gs15)) mfcolor(white) xlabel(-.1 (.05) 
> .1)  levels(95 90) xtitle("Coefficient estimate", height(6)) ///
>                                 legend(lab(3 "Personalist dummy") lab(6 "Time-varyin
> g personalism")  size(vsmall) pos(6) ring(1.5) col(4)) ///
>                                 ysize(1) xsize(1)                               

.                 graph export "$dir/golden/Collapse.pdf", as(pdf) replace
(file /Users/lee/Dropbox/Datavers/golden/Collapse.pdf written in PDF format)

.         
.                 global h="3"

.            * Logit tests *
.                  xi:xtlogit gwf_case_fail $time $covar Military Personal if Monarchy
> ==0,vce(cluster cow)
i.decade          _Idecade_0-5        (naturally coded; _Idecade_0 omitted)

Fitting comparison model:

Iteration 0:   log pseudolikelihood =  -767.9673  
Iteration 1:   log pseudolikelihood = -728.50204  
Iteration 2:   log pseudolikelihood = -712.22362  
Iteration 3:   log pseudolikelihood = -712.17647  
Iteration 4:   log pseudolikelihood = -712.17646  

Fitting full model:

tau =  0.0     log pseudolikelihood = -712.17646
tau =  0.1     log pseudolikelihood =  -711.4753
tau =  0.2     log pseudolikelihood = -712.58504

Iteration 0:   log pseudolikelihood =  -711.4753  
Iteration 1:   log pseudolikelihood = -710.83088  
Iteration 2:   log pseudolikelihood = -710.82739  
Iteration 3:   log pseudolikelihood = -710.82739  

Calculating robust standard errors:

Random-effects logistic regression              Number of obs     =      3,726
Group variable: cowcode                         Number of groups  =        105

Random effects u_i ~ Gaussian                   Obs per group:
                                                              min =          7
                                                              avg =       35.5
                                                              max =         61

Integration method: mvaghermite                 Integration pts.  =         12

                                                Wald chi2(15)     =     101.95
Log pseudolikelihood  = -710.82739              Prob > chi2       =     0.0000

                               (Std. Err. adjusted for 105 clusters in cowcode)
-------------------------------------------------------------------------------
              |               Robust
gwf_case_fail |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
--------------+----------------------------------------------------------------
     ged_time |   .0063194   .0305051     0.21   0.836    -.0534694    .0661083
    ged_time2 |  -.0000511   .0011441    -0.04   0.964    -.0022934    .0021913
    ged_time3 |   3.66e-06   .0000102     0.36   0.720    -.0000163    .0000236
   _Idecade_1 |   .3883877   .3733435     1.04   0.298    -.3433522    1.120128
   _Idecade_2 |   .2962796   .3473834     0.85   0.394    -.3845793    .9771385
   _Idecade_3 |  -.1854051   .3234401    -0.57   0.566    -.8193362    .4485259
   _Idecade_4 |  -.1806546   .3053383    -0.59   0.554    -.7791066    .4177974
   _Idecade_5 |   .4955066   .2843714     1.74   0.081    -.0618511    1.052864
      prevdem |   .4066164    .201127     2.02   0.043     .0124147    .8008181
         lgdp |  -.1709518    .119738    -1.43   0.153    -.4056339    .0637303
         grow |  -.0430127   .1079574    -0.40   0.690    -.2546054    .1685799
       intwar |   .3440327   .4824766     0.71   0.476    -.6016039    1.289669
       civwar |   .6107832   .3061275     2.00   0.046     .0107843    1.210782
     Military |   1.856376   .2330927     7.96   0.000     1.399523     2.31323
  Personalist |   .8605632   .2308035     3.73   0.000     .4081967     1.31293
        _cons |   -2.83652   .9243804    -3.07   0.002    -4.648272   -1.024768
--------------+----------------------------------------------------------------
     /lnsig2u |  -1.664597   .9432543                     -3.513341    .1841478
--------------+----------------------------------------------------------------
      sigma_u |   .4350482   .2051806                      .1726186    1.096446
          rho |   .0544006   .0485221                       .008976    .2676262
-------------------------------------------------------------------------------

.                  est store gl1

.                  xi:xtlogit gwf_case_fail $time $covar Military xirtpers8 if Monarch
> y==0, vce(cluster cow)
i.decade          _Idecade_0-5        (naturally coded; _Idecade_0 omitted)

Fitting comparison model:

Iteration 0:   log pseudolikelihood =  -767.9673  
Iteration 1:   log pseudolikelihood = -734.50253  
Iteration 2:   log pseudolikelihood = -718.65347  
Iteration 3:   log pseudolikelihood = -718.59878  
Iteration 4:   log pseudolikelihood = -718.59877  

Fitting full model:

tau =  0.0     log pseudolikelihood = -718.59877
tau =  0.1     log pseudolikelihood = -716.57092
tau =  0.2     log pseudolikelihood = -716.59102

Iteration 0:   log pseudolikelihood = -716.57092  
Iteration 1:   log pseudolikelihood =  -715.9425  
Iteration 2:   log pseudolikelihood = -714.31048  
Iteration 3:   log pseudolikelihood = -714.30713  
Iteration 4:   log pseudolikelihood = -714.30713  

Calculating robust standard errors:

Random-effects logistic regression              Number of obs     =      3,726
Group variable: cowcode                         Number of groups  =        105

Random effects u_i ~ Gaussian                   Obs per group:
                                                              min =          7
                                                              avg =       35.5
                                                              max =         61

Integration method: mvaghermite                 Integration pts.  =         12

                                                Wald chi2(15)     =      81.85
Log pseudolikelihood  = -714.30713              Prob > chi2       =     0.0000

                               (Std. Err. adjusted for 105 clusters in cowcode)
-------------------------------------------------------------------------------
              |               Robust
gwf_case_fail |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
--------------+----------------------------------------------------------------
     ged_time |   .0342846   .0341576     1.00   0.316    -.0326631    .1012323
    ged_time2 |   -.001015   .0012047    -0.84   0.399    -.0033762    .0013462
    ged_time3 |   .0000108   .0000105     1.03   0.301    -9.69e-06    .0000313
   _Idecade_1 |   .1032243   .3838921     0.27   0.788    -.6491904    .8556389
   _Idecade_2 |  -.0376122   .3558897    -0.11   0.916    -.7351431    .6599188
   _Idecade_3 |  -.4465771   .3576446    -1.25   0.212    -1.147548    .2543935
   _Idecade_4 |  -.3656868    .310297    -1.18   0.239    -.9738576    .2424841
   _Idecade_5 |   .4105018   .2939678     1.40   0.163    -.1656644     .986668
      prevdem |   .5964692   .2189374     2.72   0.006     .1673598    1.025578
         lgdp |  -.2975572   .1439268    -2.07   0.039    -.5796485   -.0154659
         grow |  -.0539071   .1133203    -0.48   0.634    -.2760109    .1681967
       intwar |   .4018001   .5307603     0.76   0.449     -.638471    1.442071
       civwar |   .6497921   .2985072     2.18   0.029     .0647287    1.234855
     Military |   1.260611   .1997375     6.31   0.000     .8691325    1.652089
    xirtpers8 |  -.9025452   .3956287    -2.28   0.023    -1.677963   -.1271272
        _cons |  -1.146507   1.080253    -1.06   0.289    -3.263764    .9707506
--------------+----------------------------------------------------------------
     /lnsig2u |  -.9077409   .5668442                     -2.018735    .2032532
--------------+----------------------------------------------------------------
      sigma_u |    .635165   .1800198                      .3644494     1.10697
          rho |   .1092341   .0551551                      .0388067    .2713875
-------------------------------------------------------------------------------

.                  est store gl2

.                  xi:clogit gwf_case_fail $time $covar Military Personal if Monarchy=
> =0,group(cow) vce(cluster cow)
i.decade          _Idecade_0-5        (naturally coded; _Idecade_0 omitted)
note: multiple positive outcomes within groups encountered.
note: 21 groups (771 obs) dropped because of all positive or
      all negative outcomes.

Iteration 0:   log pseudolikelihood = -536.85869  
Iteration 1:   log pseudolikelihood = -505.25473  
Iteration 2:   log pseudolikelihood = -498.96422  
Iteration 3:   log pseudolikelihood = -497.43458  
Iteration 4:   log pseudolikelihood = -497.42898  
Iteration 5:   log pseudolikelihood = -497.42898  

Conditional (fixed-effects) logistic regression

                                                Number of obs     =      2,955
                                                Wald chi2(15)     =     101.35
                                                Prob > chi2       =     0.0000
Log pseudolikelihood = -497.42898               Pseudo R2         =     0.1132

                                (Std. Err. adjusted for 84 clusters in cowcode)
-------------------------------------------------------------------------------
              |               Robust
gwf_case_fail |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
--------------+----------------------------------------------------------------
     ged_time |    .131048   .0498831     2.63   0.009     .0332789    .2288171
    ged_time2 |  -.0062201   .0028527    -2.18   0.029    -.0118113    -.000629
    ged_time3 |   .0001317   .0000467     2.82   0.005     .0000402    .0002233
   _Idecade_1 |   .4937551   .8499821     0.58   0.561    -1.172179    2.159689
   _Idecade_2 |   .4184135   .6950749     0.60   0.547    -.9439082    1.780735
   _Idecade_3 |  -.2100328   .5931006    -0.35   0.723    -1.372488     .952423
   _Idecade_4 |   -.304286   .5064775    -0.60   0.548    -1.296964    .6883916
   _Idecade_5 |   .4136849   .4194813     0.99   0.324    -.4084834    1.235853
      prevdem |   .5881953   .5081704     1.16   0.247    -.4078004    1.584191
         lgdp |  -.7838421   .6840989    -1.15   0.252    -2.124651     .556967
         grow |   .0378345   .1300114     0.29   0.771    -.2169831    .2926521
       intwar |   .7030638   .6602604     1.06   0.287    -.5910228     1.99715
       civwar |   1.087589   .3303594     3.29   0.001     .4400962    1.735081
     Military |   1.592057   .5041763     3.16   0.002     .6038897    2.580224
  Personalist |   .3013672   .5515657     0.55   0.585    -.7796818    1.382416
-------------------------------------------------------------------------------

.                  est store gl3

.                  xi:clogit gwf_case_fail $time $covar Military xirtpers8 if Monarchy
> ==0,group(cow) vce(cluster cow)
i.decade          _Idecade_0-5        (naturally coded; _Idecade_0 omitted)
note: multiple positive outcomes within groups encountered.
note: 21 groups (771 obs) dropped because of all positive or
      all negative outcomes.

Iteration 0:   log pseudolikelihood = -532.58271  
Iteration 1:   log pseudolikelihood = -499.50946  
Iteration 2:   log pseudolikelihood =  -493.5251  
Iteration 3:   log pseudolikelihood = -491.95645  
Iteration 4:   log pseudolikelihood = -491.94923  
Iteration 5:   log pseudolikelihood = -491.94923  

Conditional (fixed-effects) logistic regression

                                                Number of obs     =      2,955
                                                Wald chi2(15)     =     106.48
                                                Prob > chi2       =     0.0000
Log pseudolikelihood = -491.94923               Pseudo R2         =     0.1229

                                (Std. Err. adjusted for 84 clusters in cowcode)
-------------------------------------------------------------------------------
              |               Robust
gwf_case_fail |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
--------------+----------------------------------------------------------------
     ged_time |   .1637223   .0456847     3.58   0.000      .074182    .2532626
    ged_time2 |  -.0071473   .0027068    -2.64   0.008    -.0124525   -.0018421
    ged_time3 |   .0001366   .0000462     2.95   0.003      .000046    .0002273
   _Idecade_1 |   .3020152   .7143122     0.42   0.672    -1.098011    1.702041
   _Idecade_2 |   .1145431   .5789041     0.20   0.843    -1.020088    1.249174
   _Idecade_3 |  -.4014703    .533067    -0.75   0.451    -1.446262    .6433218
   _Idecade_4 |  -.3601668   .4577157    -0.79   0.431    -1.257273    .5369394
   _Idecade_5 |     .39461     .40798     0.97   0.333    -.4050162    1.194236
      prevdem |   .6514835   .4966629     1.31   0.190    -.3219579    1.624925
         lgdp |  -.8327728   .6658313    -1.25   0.211    -2.137778    .4722327
         grow |    .057858   .1278885     0.45   0.651    -.1927987    .3085148
       intwar |   .8007682   .7273296     1.10   0.271    -.6247717    2.226308
       civwar |   1.090817   .3466298     3.15   0.002     .4114353    1.770199
     Military |   1.099321   .3618396     3.04   0.002     .3901286    1.808514
    xirtpers8 |   -1.54095   .5070579    -3.04   0.002    -2.534765   -.5471344
-------------------------------------------------------------------------------

.                  est store gl4

.                  coefplot (gl1, msymbol(d)) (gl2, msymbol(t)) (gl3, msymbol(s)) (gl4
> , msymbol(s)), title("Logit", size(small)) ///
>                                 scheme(plottig) drop(_cons _Idecade_* $time $covar) 
> xline(0) ///
>                                 grid(glcolor(gs15)) mfcolor(white) xlabel(-2 (1) 2) 
>  levels(95 90) xtitle("Coefficient estimate", height($h)size(small)) ///
>                                 legend(lab(3 "RE dummy") lab(6 "RE time-vary") lab(9
>  "FE dummy") lab(12 "FE time-vary")size(vsmall) pos(6) ring(1.5) col(2)) ///
>                                 ysize(1) xsize(1)                 saving(r1, replace
> )           
(note: file r1.gph not found)
(file r1.gph saved)

.                 * Add monarchies to the sample *
.                  xi:xtreg gwf_case_fail $time $covar Military Monarchy Personal,vce(
> cluster cow)
i.decade          _Idecade_0-5        (naturally coded; _Idecade_0 omitted)

Random-effects GLS regression                   Number of obs     =      4,250
Group variable: cowcode                         Number of groups  =        113

R-sq:                                           Obs per group:
     within  = 0.0145                                         min =          7
     between = 0.2860                                         avg =       37.6
     overall = 0.0346                                         max =         61

                                                Wald chi2(16)     =      99.72
corr(u_i, X)   = 0 (assumed)                    Prob > chi2       =     0.0000

                              (Std. Err. adjusted for 113 clusters in cowcode)
------------------------------------------------------------------------------
             |               Robust
gwf_case_f~l |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    ged_time |   .0007771   .0013126     0.59   0.554    -.0017956    .0033497
   ged_time2 |  -.0000308   .0000453    -0.68   0.497    -.0001196     .000058
   ged_time3 |   3.90e-07   4.11e-07     0.95   0.342    -4.15e-07    1.19e-06
  _Idecade_1 |   .0207303   .0146842     1.41   0.158    -.0080501    .0495107
  _Idecade_2 |   .0178655   .0139936     1.28   0.202    -.0095615    .0452924
  _Idecade_3 |   .0001465   .0113318     0.01   0.990    -.0220635    .0223564
  _Idecade_4 |    -.00485   .0105994    -0.46   0.647    -.0256244    .0159244
  _Idecade_5 |   .0252903   .0118835     2.13   0.033     .0019991    .0485815
     prevdem |    .018075   .0105154     1.72   0.086    -.0025349    .0386848
        lgdp |  -.0064249    .004205    -1.53   0.127    -.0146666    .0018167
        grow |  -.0036565   .0043692    -0.84   0.403    -.0122201    .0049071
      intwar |   .0145401   .0239252     0.61   0.543    -.0323525    .0614327
      civwar |   .0415126   .0229435     1.81   0.070    -.0034558     .086481
    Military |   .1026738   .0171407     5.99   0.000     .0690787    .1362689
    Monarchy |  -.0062787   .0074021    -0.85   0.396    -.0207866    .0082292
 Personalist |   .0322825   .0084374     3.83   0.000     .0157455    .0488196
       _cons |   .0566517   .0349385     1.62   0.105    -.0118264    .1251298
-------------+----------------------------------------------------------------
     sigma_u |          0
     sigma_e |  .20996971
         rho |          0   (fraction of variance due to u_i)
------------------------------------------------------------------------------

.                  est store m1

.                  xi:xtreg gwf_case_fail $time $covar Military Monarchy xirtpers8,vce
> (cluster cow)
i.decade          _Idecade_0-5        (naturally coded; _Idecade_0 omitted)

Random-effects GLS regression                   Number of obs     =      4,250
Group variable: cowcode                         Number of groups  =        113

R-sq:                                           Obs per group:
     within  = 0.0163                                         min =          7
     between = 0.2695                                         avg =       37.6
     overall = 0.0327                                         max =         61

                                                Wald chi2(16)     =      72.20
corr(u_i, X)   = 0 (assumed)                    Prob > chi2       =     0.0000

                              (Std. Err. adjusted for 113 clusters in cowcode)
------------------------------------------------------------------------------
             |               Robust
gwf_case_f~l |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    ged_time |   .0008456   .0013667     0.62   0.536     -.001833    .0035242
   ged_time2 |  -.0000449   .0000468    -0.96   0.337    -.0001368    .0000469
   ged_time3 |   5.18e-07   4.20e-07     1.23   0.218    -3.06e-07    1.34e-06
  _Idecade_1 |   .0066635   .0143826     0.46   0.643    -.0215259    .0348529
  _Idecade_2 |    .004391   .0137193     0.32   0.749    -.0224983    .0312803
  _Idecade_3 |  -.0089784   .0120439    -0.75   0.456     -.032584    .0146272
  _Idecade_4 |  -.0113377   .0104235    -1.09   0.277    -.0317674    .0090921
  _Idecade_5 |   .0215209   .0118882     1.81   0.070    -.0017795    .0448213
     prevdem |   .0236276   .0107533     2.20   0.028     .0025514    .0447037
        lgdp |  -.0094811    .004405    -2.15   0.031    -.0181148   -.0008474
        grow |  -.0039307   .0043733    -0.90   0.369    -.0125022    .0046409
      intwar |   .0108581    .025894     0.42   0.675    -.0398931    .0616094
      civwar |    .038654   .0232077     1.67   0.096    -.0068323    .0841402
    Military |   .0815632   .0166045     4.91   0.000     .0490189    .1141075
    Monarchy |  -.0104828   .0074727    -1.40   0.161    -.0251291    .0041635
   xirtpers8 |  -.0298373   .0136529    -2.19   0.029    -.0565964   -.0030782
       _cons |   .1150616   .0380118     3.03   0.002     .0405599    .1895634
-------------+----------------------------------------------------------------
     sigma_u |          0
     sigma_e |  .20954757
         rho |          0   (fraction of variance due to u_i)
------------------------------------------------------------------------------

.                  est store m2

.                  xi:xtreg gwf_case_fail $time $covar Military Monarchy Personal,fe v
> ce(cluster cow)
i.decade          _Idecade_0-5        (naturally coded; _Idecade_0 omitted)

Fixed-effects (within) regression               Number of obs     =      4,250
Group variable: cowcode                         Number of groups  =        113

R-sq:                                           Obs per group:
     within  = 0.0233                                         min =          7
     between = 0.0430                                         avg =       37.6
     overall = 0.0198                                         max =         61

                                                F(16,112)         =       5.26
corr(u_i, Xb)  = -0.2625                        Prob > F          =     0.0000

                              (Std. Err. adjusted for 113 clusters in cowcode)
------------------------------------------------------------------------------
             |               Robust
gwf_case_f~l |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    ged_time |   .0034776   .0013302     2.61   0.010     .0008419    .0061132
   ged_time2 |  -.0000661   .0000445    -1.48   0.140    -.0001544    .0000221
   ged_time3 |   6.39e-07   3.67e-07     1.74   0.084    -8.83e-08    1.37e-06
  _Idecade_1 |   .0093559   .0290312     0.32   0.748    -.0481658    .0668775
  _Idecade_2 |   .0125993   .0252403     0.50   0.619    -.0374112    .0626097
  _Idecade_3 |  -.0074537   .0195042    -0.38   0.703    -.0460989    .0311915
  _Idecade_4 |  -.0167284   .0166884    -1.00   0.318    -.0497944    .0163375
  _Idecade_5 |   .0223315    .012867     1.74   0.085    -.0031628    .0478257
     prevdem |   .0350326   .0361262     0.97   0.334    -.0365469     .106612
        lgdp |   -.019811   .0176733    -1.12   0.265    -.0548285    .0152064
        grow |  -.0012289   .0048246    -0.25   0.799    -.0107881    .0083304
      intwar |   .0520688   .0309693     1.68   0.095    -.0092929    .1134305
      civwar |   .0699379   .0248189     2.82   0.006     .0207625    .1191133
    Military |   .0981443   .0314545     3.12   0.002     .0358211    .1604674
    Monarchy |   .0018843   .0341471     0.06   0.956    -.0657738    .0695425
 Personalist |   .0045421   .0253228     0.18   0.858    -.0456318    .0547159
       _cons |   .1280024   .1353599     0.95   0.346     -.140196    .3962008
-------------+----------------------------------------------------------------
     sigma_u |  .07119818
     sigma_e |  .20996971
         rho |  .10312338   (fraction of variance due to u_i)
------------------------------------------------------------------------------

.                  est store m3

.                  xi:xtreg gwf_case_fail $time $covar Military Monarchy xirtpers8,fe 
> vce(cluster cow)
i.decade          _Idecade_0-5        (naturally coded; _Idecade_0 omitted)

Fixed-effects (within) regression               Number of obs     =      4,250
Group variable: cowcode                         Number of groups  =        113

R-sq:                                           Obs per group:
     within  = 0.0273                                         min =          7
     between = 0.0409                                         avg =       37.6
     overall = 0.0191                                         max =         61

                                                F(16,112)         =       5.81
corr(u_i, Xb)  = -0.3462                        Prob > F          =     0.0000

                              (Std. Err. adjusted for 113 clusters in cowcode)
------------------------------------------------------------------------------
             |               Robust
gwf_case_f~l |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    ged_time |   .0047683   .0014121     3.38   0.001     .0019704    .0075661
   ged_time2 |  -.0001033   .0000456    -2.26   0.025    -.0001938   -.0000129
   ged_time3 |   9.02e-07   3.72e-07     2.43   0.017     1.65e-07    1.64e-06
  _Idecade_1 |  -.0000602   .0257556    -0.00   0.998    -.0510916    .0509711
  _Idecade_2 |   .0042809   .0218717     0.20   0.845    -.0390551    .0476169
  _Idecade_3 |  -.0107209   .0177959    -0.60   0.548    -.0459811    .0245393
  _Idecade_4 |  -.0166257   .0152362    -1.09   0.278    -.0468143    .0135628
  _Idecade_5 |   .0224929   .0129157     1.74   0.084    -.0030979    .0480838
     prevdem |   .0411596   .0358025     1.15   0.253    -.0297784    .1120976
        lgdp |  -.0236241   .0170706    -1.38   0.169    -.0574472    .0101991
        grow |  -.0009318   .0047341    -0.20   0.844    -.0103117    .0084481
      intwar |   .0528785   .0315111     1.68   0.096    -.0095567    .1153138
      civwar |   .0675369   .0246387     2.74   0.007     .0187185    .1163554
    Military |   .0816311   .0291615     2.80   0.006     .0238513    .1394108
    Monarchy |   -.002152   .0347424    -0.06   0.951    -.0709898    .0666857
   xirtpers8 |  -.0752094   .0209114    -3.60   0.000    -.1166426   -.0337762
       _cons |   .1864756   .1305259     1.43   0.156    -.0721447    .4450959
-------------+----------------------------------------------------------------
     sigma_u |  .07274866
     sigma_e |  .20954757
         rho |  .10756287   (fraction of variance due to u_i)
------------------------------------------------------------------------------

.                  est store m4

.                  coefplot (m1, msymbol(d)) (m2, msymbol(t)) (m3, msymbol(s)) (m4, ms
> ymbol(s)), title("Add monarchies", size(small)) ///
>                                 scheme(plottig) drop(_cons _Idecade_* $time $covar) 
> xline(0) ///
>                                 grid(glcolor(gs15)) mfcolor(white) xlabel(-.1 (.05) 
> .1)   levels(95 90) xtitle("Coefficient estimate", height($h)size(small)) ///
>                                 legend(lab(3 "RE dummy") lab(6 "RE time-vary") lab(9
>  "FE dummy") lab(12 "FE time-vary")size(vsmall) pos(6) ring(1.5) col(2)) ///
>                                 ysize(1) xsize(1) saving(r2, replace)           
(note: file r2.gph not found)
(file r2.gph saved)

.                 * Add monarchies to the sample but drop other regime type variables 
> *
.                  xi:xtreg gwf_case_fail $time $covar Personal ,vce(cluster cow)
i.decade          _Idecade_0-5        (naturally coded; _Idecade_0 omitted)

Random-effects GLS regression                   Number of obs     =      4,250
Group variable: cowcode                         Number of groups  =        113

R-sq:                                           Obs per group:
     within  = 0.0027                                         min =          7
     between = 0.2242                                         avg =       37.6
     overall = 0.0159                                         max =         61

                                                Wald chi2(14)     =      69.83
corr(u_i, X)   = 0 (assumed)                    Prob > chi2       =     0.0000

                              (Std. Err. adjusted for 113 clusters in cowcode)
------------------------------------------------------------------------------
             |               Robust
gwf_case_f~l |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    ged_time |  -.0018241   .0014553    -1.25   0.210    -.0046765    .0010282
   ged_time2 |   .0000143   .0000488     0.29   0.769    -.0000812    .0001099
   ged_time3 |   1.36e-07   4.42e-07     0.31   0.758    -7.30e-07    1.00e-06
  _Idecade_1 |   .0066483   .0141948     0.47   0.640     -.021173    .0344695
  _Idecade_2 |   .0078546    .013935     0.56   0.573    -.0194574    .0351666
  _Idecade_3 |  -.0014665   .0114856    -0.13   0.898    -.0239779    .0210449
  _Idecade_4 |   -.001574   .0105785    -0.15   0.882    -.0223074    .0191595
  _Idecade_5 |   .0259931   .0125144     2.08   0.038     .0014654    .0505208
     prevdem |   .0369291   .0112917     3.27   0.001     .0147978    .0590605
        lgdp |  -.0056334   .0046321    -1.22   0.224    -.0147121    .0034453
        grow |  -.0020661   .0044704    -0.46   0.644     -.010828    .0066958
      intwar |   .0034564   .0263283     0.13   0.896    -.0481461    .0550589
      civwar |   .0382523   .0206452     1.85   0.064    -.0022115    .0787162
 Personalist |   .0039747   .0088285     0.45   0.653    -.0133288    .0212782
       _cons |   .0979183   .0380981     2.57   0.010     .0232474    .1725892
-------------+----------------------------------------------------------------
     sigma_u |          0
     sigma_e |  .21069148
         rho |          0   (fraction of variance due to u_i)
------------------------------------------------------------------------------

.                  est store a1

.                  xi:xtreg gwf_case_fail $time $covar xirtpers8 ,vce(cluster cow)
i.decade          _Idecade_0-5        (naturally coded; _Idecade_0 omitted)

Random-effects GLS regression                   Number of obs     =      4,250
Group variable: cowcode                         Number of groups  =        113

R-sq:                                           Obs per group:
     within  = 0.0066                                         min =          7
     between = 0.2245                                         avg =       37.6
     overall = 0.0195                                         max =         61

                                                Wald chi2(14)     =      71.33
corr(u_i, X)   = 0 (assumed)                    Prob > chi2       =     0.0000

                              (Std. Err. adjusted for 113 clusters in cowcode)
------------------------------------------------------------------------------
             |               Robust
gwf_case_f~l |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    ged_time |  -.0005789   .0014447    -0.40   0.689    -.0034105    .0022526
   ged_time2 |   -.000024   .0000488    -0.49   0.623    -.0001196    .0000717
   ged_time3 |   4.10e-07   4.39e-07     0.93   0.350    -4.50e-07    1.27e-06
  _Idecade_1 |  -.0016985   .0141868    -0.12   0.905    -.0295041    .0261071
  _Idecade_2 |  -.0000853    .013729    -0.01   0.995    -.0269936    .0268231
  _Idecade_3 |  -.0062113   .0118787    -0.52   0.601     -.029493    .0170705
  _Idecade_4 |  -.0047587   .0105731    -0.45   0.653    -.0254815    .0159641
  _Idecade_5 |   .0240783   .0125405     1.92   0.055    -.0005006    .0486573
     prevdem |   .0355345   .0114294     3.11   0.002     .0131332    .0579358
        lgdp |   -.008464   .0046605    -1.82   0.069    -.0175985    .0006705
        grow |   -.002245   .0044323    -0.51   0.612    -.0109322    .0064421
      intwar |    .002254   .0274801     0.08   0.935    -.0516061     .056114
      civwar |   .0347132   .0211786     1.64   0.101    -.0067961    .0762226
   xirtpers8 |   -.051873   .0157459    -3.29   0.001    -.0827345   -.0210115
       _cons |    .140267   .0395352     3.55   0.000     .0627794    .2177547
-------------+----------------------------------------------------------------
     sigma_u |          0
     sigma_e |  .21012984
         rho |          0   (fraction of variance due to u_i)
------------------------------------------------------------------------------

.                  est store a2

.                  xi:xtreg gwf_case_fail $time $covar  Personal ,fe vce(cluster cow)
i.decade          _Idecade_0-5        (naturally coded; _Idecade_0 omitted)

Fixed-effects (within) regression               Number of obs     =      4,250
Group variable: cowcode                         Number of groups  =        113

R-sq:                                           Obs per group:
     within  = 0.0161                                         min =          7
     between = 0.0839                                         avg =       37.6
     overall = 0.0015                                         max =         61

                                                F(14,112)         =       3.68
corr(u_i, Xb)  = -0.4335                        Prob > F          =     0.0000

                              (Std. Err. adjusted for 113 clusters in cowcode)
------------------------------------------------------------------------------
             |               Robust
gwf_case_f~l |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    ged_time |   .0024262   .0015448     1.57   0.119    -.0006345     .005487
   ged_time2 |  -.0000518   .0000484    -1.07   0.287    -.0001477    .0000441
   ged_time3 |   5.53e-07   3.97e-07     1.39   0.166    -2.33e-07    1.34e-06
  _Idecade_1 |   -.012825   .0254286    -0.50   0.615    -.0632085    .0375585
  _Idecade_2 |  -.0051751    .021966    -0.24   0.814     -.048698    .0383478
  _Idecade_3 |  -.0184655   .0180541    -1.02   0.309    -.0542373    .0173064
  _Idecade_4 |  -.0219389   .0158076    -1.39   0.168    -.0532597    .0093819
  _Idecade_5 |   .0201011   .0130392     1.54   0.126    -.0057345    .0459367
     prevdem |   .0371964   .0368003     1.01   0.314    -.0357188    .1101115
        lgdp |  -.0188642   .0165751    -1.14   0.258    -.0517056    .0139772
        grow |  -.0005508   .0048982    -0.11   0.911    -.0102559    .0091544
      intwar |    .054506   .0319085     1.71   0.090    -.0087166    .1177286
      civwar |    .071432   .0232338     3.07   0.003     .0253972    .1174668
 Personalist |  -.0334501    .024777    -1.35   0.180    -.0825425    .0156422
       _cons |   .1657115   .1297585     1.28   0.204    -.0913883    .4228113
-------------+----------------------------------------------------------------
     sigma_u |  .08162622
     sigma_e |  .21069148
         rho |   .1305064   (fraction of variance due to u_i)
------------------------------------------------------------------------------

.                  est store a3

.                  xi:xtreg gwf_case_fail $time $covar xirtpers8,fe vce(cluster cow)
i.decade          _Idecade_0-5        (naturally coded; _Idecade_0 omitted)

Fixed-effects (within) regression               Number of obs     =      4,250
Group variable: cowcode                         Number of groups  =        113

R-sq:                                           Obs per group:
     within  = 0.0214                                         min =          7
     between = 0.0135                                         avg =       37.6
     overall = 0.0058                                         max =         61

                                                F(14,112)         =       5.33
corr(u_i, Xb)  = -0.4147                        Prob > F          =     0.0000

                              (Std. Err. adjusted for 113 clusters in cowcode)
------------------------------------------------------------------------------
             |               Robust
gwf_case_f~l |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    ged_time |   .0043852   .0014736     2.98   0.004     .0014655    .0073049
   ged_time2 |  -.0001011   .0000466    -2.17   0.032    -.0001935   -8.82e-06
   ged_time3 |   8.89e-07   3.80e-07     2.34   0.021     1.37e-07    1.64e-06
  _Idecade_1 |  -.0125876   .0227176    -0.55   0.581    -.0575997    .0324245
  _Idecade_2 |  -.0037021   .0192683    -0.19   0.848    -.0418799    .0344756
  _Idecade_3 |   -.013485     .01648    -0.82   0.415     -.046138     .019168
  _Idecade_4 |  -.0158147   .0147515    -1.07   0.286    -.0450429    .0134135
  _Idecade_5 |   .0231317   .0131486     1.76   0.081    -.0029206    .0491839
     prevdem |   .0363708   .0365853     0.99   0.322    -.0361183    .1088598
        lgdp |  -.0212888   .0160573    -1.33   0.188    -.0531042    .0105267
        grow |  -.0007872   .0047803    -0.16   0.869    -.0102589    .0086844
      intwar |    .053262   .0309122     1.72   0.088    -.0079866    .1145105
      civwar |   .0700655   .0236758     2.96   0.004     .0231549    .1169761
   xirtpers8 |  -.0949081   .0237818    -3.99   0.000    -.1420287   -.0477875
       _cons |   .1980815   .1251234     1.58   0.116    -.0498344    .4459975
-------------+----------------------------------------------------------------
     sigma_u |  .07957587
     sigma_e |  .21012984
         rho |  .12542505   (fraction of variance due to u_i)
------------------------------------------------------------------------------

.                  est store a4

.                   coefplot (a1, msymbol(d)) (a2, msymbol(t)) (a3, msymbol(s)) (a4, m
> symbol(s)), title("Add monarchies, drop regime dummies", size(small)) ///
>                                 scheme(plottig) drop(_cons _Idecade_* $time $covar) 
> xline(0) ///
>                                 grid(glcolor(gs15)) mfcolor(white)xlabel(-.1 (.05) .
> 1)levels(95 90) xtitle("Coefficient estimate", height($h)size(small)) ///
>                                 legend(lab(3 "RE dummy") lab(6 "RE time-vary") lab(9
>  "FE dummy") lab(12 "FE time-vary")size(vsmall) pos(6) ring(1.5) col(2)) ///
>                                 ysize(1) xsize(1) saving(r3, replace)   
(note: file r3.gph not found)
(file r3.gph saved)

.                 * Add monarchies to the sample but drop covariates *                
>     
.                  xi:xtreg gwf_case_fail $time Military Monarchy Personal,vce(cluster
>  cow)
i.decade          _Idecade_0-5        (naturally coded; _Idecade_0 omitted)

Random-effects GLS regression                   Number of obs     =      4,591
Group variable: cowcode                         Number of groups  =        118

R-sq:                                           Obs per group:
     within  = 0.0142                                         min =          1
     between = 0.1179                                         avg =       38.9
     overall = 0.0305                                         max =         65

                                                Wald chi2(11)     =      92.88
corr(u_i, X)   = 0 (assumed)                    Prob > chi2       =     0.0000

                              (Std. Err. adjusted for 118 clusters in cowcode)
------------------------------------------------------------------------------
             |               Robust
gwf_case_f~l |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    ged_time |   .0006385   .0010411     0.61   0.540    -.0014021     .002679
   ged_time2 |  -7.27e-06   .0000325    -0.22   0.823     -.000071    .0000564
   ged_time3 |   1.46e-07   2.59e-07     0.56   0.573    -3.62e-07    6.54e-07
  _Idecade_1 |   .0208057   .0178686     1.16   0.244    -.0142162    .0558275
  _Idecade_2 |   .0145937   .0157431     0.93   0.354    -.0162622    .0454496
  _Idecade_3 |  -.0036568   .0128708    -0.28   0.776    -.0288831    .0215696
  _Idecade_4 |  -.0078213   .0120233    -0.65   0.515    -.0313865     .015744
  _Idecade_5 |   .0308427   .0120511     2.56   0.010     .0072229    .0544625
    Military |   .1116306   .0177858     6.28   0.000     .0767711    .1464901
    Monarchy |  -.0105728   .0124204    -0.85   0.395    -.0349162    .0137706
 Personalist |   .0391772   .0123085     3.18   0.001      .015053    .0633015
       _cons |   .0101802   .0148858     0.68   0.494    -.0189954    .0393557
-------------+----------------------------------------------------------------
     sigma_u |  .04579692
     sigma_e |  .20943337
         rho |  .04563472   (fraction of variance due to u_i)
------------------------------------------------------------------------------

.                  est store mc1

.                  xi:xtreg gwf_case_fail $time Military Monarchy xirtpers8,vce(cluste
> r cow)
i.decade          _Idecade_0-5        (naturally coded; _Idecade_0 omitted)

Random-effects GLS regression                   Number of obs     =      4,591
Group variable: cowcode                         Number of groups  =        118

R-sq:                                           Obs per group:
     within  = 0.0163                                         min =          1
     between = 0.0563                                         avg =       38.9
     overall = 0.0247                                         max =         65

                                                Wald chi2(11)     =      62.64
corr(u_i, X)   = 0 (assumed)                    Prob > chi2       =     0.0000

                              (Std. Err. adjusted for 118 clusters in cowcode)
------------------------------------------------------------------------------
             |               Robust
gwf_case_f~l |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    ged_time |   .0011033   .0010923     1.01   0.312    -.0010375    .0032442
   ged_time2 |  -.0000339   .0000337    -1.01   0.315    -.0001001    .0000322
   ged_time3 |   3.65e-07   2.68e-07     1.36   0.173    -1.60e-07    8.89e-07
  _Idecade_1 |   .0045292   .0164581     0.28   0.783    -.0277281    .0367864
  _Idecade_2 |   -.002108   .0148582    -0.14   0.887    -.0312296    .0270136
  _Idecade_3 |  -.0153574   .0132401    -1.16   0.246    -.0413076    .0105928
  _Idecade_4 |  -.0152944   .0115673    -1.32   0.186    -.0379659     .007377
  _Idecade_5 |    .026995   .0119862     2.25   0.024     .0035026    .0504874
    Military |   .0844922   .0165492     5.11   0.000     .0520564     .116928
    Monarchy |   -.018971   .0129082    -1.47   0.142    -.0442707    .0063286
   xirtpers8 |  -.0469551   .0152866    -3.07   0.002    -.0769162   -.0169939
       _cons |   .0560078   .0153227     3.66   0.000      .025976    .0860397
-------------+----------------------------------------------------------------
     sigma_u |  .04596499
     sigma_e |  .20914348
         rho |  .04607647   (fraction of variance due to u_i)
------------------------------------------------------------------------------

.                  est store mc2

.                  xi:xtreg gwf_case_fail $time Military Monarchy Personal,fe vce(clus
> ter cow)
i.decade          _Idecade_0-5        (naturally coded; _Idecade_0 omitted)

Fixed-effects (within) regression               Number of obs     =      4,591
Group variable: cowcode                         Number of groups  =        118

R-sq:                                           Obs per group:
     within  = 0.0160                                         min =          1
     between = 0.0288                                         avg =       38.9
     overall = 0.0193                                         max =         65

                                                F(11,117)         =       6.25
corr(u_i, Xb)  = -0.0900                        Prob > F          =     0.0000

                              (Std. Err. adjusted for 118 clusters in cowcode)
------------------------------------------------------------------------------
             |               Robust
gwf_case_f~l |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    ged_time |   .0016266   .0011643     1.40   0.165    -.0006792    .0039324
   ged_time2 |  -.0000195   .0000367    -0.53   0.596    -.0000923    .0000532
   ged_time3 |   2.06e-07   2.92e-07     0.71   0.482    -3.72e-07    7.84e-07
  _Idecade_1 |   .0135139   .0272533     0.50   0.621    -.0404599    .0674876
  _Idecade_2 |    .011201   .0229206     0.49   0.626     -.034192    .0565939
  _Idecade_3 |  -.0092096   .0179657    -0.51   0.609    -.0447898    .0263705
  _Idecade_4 |  -.0138549   .0155871    -0.89   0.376    -.0447243    .0170145
  _Idecade_5 |   .0295194   .0126022     2.34   0.021     .0045613    .0544774
    Military |    .099485   .0301662     3.30   0.001     .0397424    .1592276
    Monarchy |   .0049014   .0289178     0.17   0.866    -.0523688    .0621716
 Personalist |   .0243792   .0239803     1.02   0.311    -.0231125    .0718709
       _cons |   .0006309    .023392     0.03   0.979    -.0456958    .0469576
-------------+----------------------------------------------------------------
     sigma_u |   .1056342
     sigma_e |  .20943337
         rho |  .20280621   (fraction of variance due to u_i)
------------------------------------------------------------------------------

.                  est store mc3

.                  xi:xtreg gwf_case_fail $time Military Monarchy xirtpers8,fe vce(clu
> ster cow)
i.decade          _Idecade_0-5        (naturally coded; _Idecade_0 omitted)

Fixed-effects (within) regression               Number of obs     =      4,591
Group variable: cowcode                         Number of groups  =        118

R-sq:                                           Obs per group:
     within  = 0.0188                                         min =          1
     between = 0.0024                                         avg =       38.9
     overall = 0.0130                                         max =         65

                                                F(11,117)         =       5.10
corr(u_i, Xb)  = -0.2315                        Prob > F          =     0.0000

                              (Std. Err. adjusted for 118 clusters in cowcode)
------------------------------------------------------------------------------
             |               Robust
gwf_case_f~l |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    ged_time |   .0026326   .0012721     2.07   0.041     .0001134    .0051519
   ged_time2 |  -.0000546    .000038    -1.44   0.154      -.00013    .0000207
   ged_time3 |   4.68e-07   2.97e-07     1.57   0.119    -1.21e-07    1.06e-06
  _Idecade_1 |   .0009097   .0237289     0.04   0.969    -.0460841    .0479035
  _Idecade_2 |  -.0021026   .0198895    -0.11   0.916    -.0414928    .0372876
  _Idecade_3 |  -.0175277   .0164835    -1.06   0.290    -.0501723     .015117
  _Idecade_4 |  -.0177716   .0142793    -1.24   0.216     -.046051    .0105078
  _Idecade_5 |   .0278262     .01247     2.23   0.028     .0031301    .0525223
    Military |   .0727126   .0272963     2.66   0.009     .0186537    .1267716
    Monarchy |  -.0034321    .029808    -0.12   0.909    -.0624652    .0556011
   xirtpers8 |  -.0673738   .0205725    -3.27   0.001    -.1081166   -.0266311
       _cons |   .0419992   .0185144     2.27   0.025     .0053323     .078666
-------------+----------------------------------------------------------------
     sigma_u |  .10887505
     sigma_e |  .20914348
         rho |  .21321746   (fraction of variance due to u_i)
------------------------------------------------------------------------------

.                  est store mc4

.                  coefplot (mc1, msymbol(d)) (mc2, msymbol(t)) (mc3, msymbol(s)) (mc4
> , msymbol(s)), title("Add monarchies, drop covariates", size(small)) ///
>                                 scheme(plottig) drop(_cons _Idecade_* $time) xline(0
> ) ///
>                                 grid(glcolor(gs15)) mfcolor(white) xlabel(-.1 (.05) 
> .1) levels(95 90) xtitle("Coefficient estimate", height($h)size(small)) ///
>                                 legend(lab(3 "RE dummy") lab(6 "RE time-vary") lab(9
>  "FE dummy") lab(12 "FE time-vary")size(vsmall) pos(6) ring(1.5) col(2)) ///
>                                 ysize(1) xsize(1) saving(r4, replace)               
>             
(note: file r4.gph not found)
(file r4.gph saved)

.                 gr combine r1.gph r2.gph r3.gph r4.gph, col(2) xsize(1.75) ysize(1.9
> 5) iscale(.68)

.                 graph export "$dir/golden/Collapse-Robust.pdf", as(pdf) replace
(file /Users/lee/Dropbox/Datavers/golden/Collapse-Robust.pdf written in PDF format)

.                         
.                  xi:xtreg ged_dem $time $covar Military Personal if Monarchy==0,vce(
> cluster cow)
i.decade          _Idecade_0-5        (naturally coded; _Idecade_0 omitted)

Random-effects GLS regression                   Number of obs     =      3,726
Group variable: cowcode                         Number of groups  =        105

R-sq:                                           Obs per group:
     within  = 0.0309                                         min =          7
     between = 0.2250                                         avg =       35.5
     overall = 0.0402                                         max =         61

                                                Wald chi2(15)     =     104.15
corr(u_i, X)   = 0 (assumed)                    Prob > chi2       =     0.0000

                              (Std. Err. adjusted for 105 clusters in cowcode)
------------------------------------------------------------------------------
             |               Robust
     ged_dem |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    ged_time |   .0011396   .0012079     0.94   0.345    -.0012279     .003507
   ged_time2 |  -.0000445   .0000418    -1.06   0.288    -.0001264    .0000375
   ged_time3 |   5.51e-07   3.73e-07     1.48   0.140    -1.80e-07    1.28e-06
  _Idecade_1 |  -.0147781   .0136826    -1.08   0.280    -.0415955    .0120392
  _Idecade_2 |  -.0205269   .0110883    -1.85   0.064    -.0422595    .0012057
  _Idecade_3 |  -.0281644   .0099745    -2.82   0.005    -.0477141   -.0086146
  _Idecade_4 |  -.0217792   .0096937    -2.25   0.025    -.0407786   -.0027798
  _Idecade_5 |   .0135228   .0110168     1.23   0.220    -.0080698    .0351153
     prevdem |   .0174751   .0084378     2.07   0.038     .0009374    .0340128
        lgdp |    .002744   .0040228     0.68   0.495    -.0051406    .0106286
        grow |  -.0024515   .0032265    -0.76   0.447    -.0087754    .0038724
      intwar |   .0007196   .0100631     0.07   0.943    -.0190036    .0204429
      civwar |  -.0047076   .0164775    -0.29   0.775    -.0370028    .0275877
    Military |   .0809866   .0135057     6.00   0.000     .0545158    .1074573
 Personalist |   .0085411   .0067829     1.26   0.208    -.0047532    .0218354
       _cons |  -.0078847    .030898    -0.26   0.799    -.0684436    .0526742
-------------+----------------------------------------------------------------
     sigma_u |  .01676272
     sigma_e |  .15189195
         rho |  .01203267   (fraction of variance due to u_i)
------------------------------------------------------------------------------

.                  est store g1

.                  xi:xtreg ged_dict $time $covar Military Personal if Monarchy==0, vc
> e(cluster cow)
i.decade          _Idecade_0-5        (naturally coded; _Idecade_0 omitted)

Random-effects GLS regression                   Number of obs     =      3,726
Group variable: cowcode                         Number of groups  =        105

R-sq:                                           Obs per group:
     within  = 0.0047                                         min =          7
     between = 0.2317                                         avg =       35.5
     overall = 0.0151                                         max =         61

                                                Wald chi2(15)     =      52.26
corr(u_i, X)   = 0 (assumed)                    Prob > chi2       =     0.0000

                              (Std. Err. adjusted for 105 clusters in cowcode)
------------------------------------------------------------------------------
             |               Robust
    ged_dict |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    ged_time |  -.0010715   .0009749    -1.10   0.272    -.0029823    .0008393
   ged_time2 |   .0000362   .0000329     1.10   0.271    -.0000283    .0001007
   ged_time3 |  -2.99e-07   3.05e-07    -0.98   0.327    -8.97e-07    2.99e-07
  _Idecade_1 |   .0287417   .0122098     2.35   0.019      .004811    .0526724
  _Idecade_2 |   .0321276   .0109716     2.93   0.003     .0106237    .0536316
  _Idecade_3 |    .018141   .0076134     2.38   0.017      .003219     .033063
  _Idecade_4 |   .0113537    .008263     1.37   0.169    -.0048415    .0275489
  _Idecade_5 |    .010872   .0077119     1.41   0.159    -.0042431    .0259871
     prevdem |  -.0019193     .00808    -0.24   0.812    -.0177557    .0139172
        lgdp |  -.0078462   .0033328    -2.35   0.019    -.0143783    -.001314
        grow |  -.0005545   .0042849    -0.13   0.897    -.0089526    .0078437
      intwar |   .0189547   .0231922     0.82   0.414    -.0265011    .0644105
      civwar |   .0426245      .0199     2.14   0.032     .0036213    .0816277
    Military |   .0264317   .0111754     2.37   0.018     .0045283     .048335
 Personalist |   .0241753   .0072986     3.31   0.001     .0098703    .0384802
       _cons |   .0636062   .0249559     2.55   0.011     .0146936    .1125188
-------------+----------------------------------------------------------------
     sigma_u |          0
     sigma_e |  .16117046
         rho |          0   (fraction of variance due to u_i)
------------------------------------------------------------------------------

.                  est store g2

.                  xi:xtreg ged_dem $time $covar Military xirtpers8 if Monarchy==0,vce
> (cluster cow)
i.decade          _Idecade_0-5        (naturally coded; _Idecade_0 omitted)

Random-effects GLS regression                   Number of obs     =      3,726
Group variable: cowcode                         Number of groups  =        105

R-sq:                                           Obs per group:
     within  = 0.0347                                         min =          7
     between = 0.2282                                         avg =       35.5
     overall = 0.0418                                         max =         61

                                                Wald chi2(15)     =     101.14
corr(u_i, X)   = 0 (assumed)                    Prob > chi2       =     0.0000

                              (Std. Err. adjusted for 105 clusters in cowcode)
------------------------------------------------------------------------------
             |               Robust
     ged_dem |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    ged_time |   .0017891   .0012339     1.45   0.147    -.0006292    .0042074
   ged_time2 |  -.0000677    .000043    -1.57   0.116    -.0001521    .0000166
   ged_time3 |   7.10e-07   3.81e-07     1.86   0.063    -3.73e-08    1.46e-06
  _Idecade_1 |  -.0219536   .0133975    -1.64   0.101    -.0482122    .0043049
  _Idecade_2 |    -.02812   .0108897    -2.58   0.010    -.0494634   -.0067767
  _Idecade_3 |  -.0325972   .0100837    -3.23   0.001    -.0523608   -.0128335
  _Idecade_4 |  -.0240864   .0096292    -2.50   0.012    -.0429592   -.0052136
  _Idecade_5 |   .0120734   .0110584     1.09   0.275    -.0096006    .0337473
     prevdem |   .0189709   .0083243     2.28   0.023     .0026557    .0352862
        lgdp |   .0010784   .0040046     0.27   0.788    -.0067705    .0089273
        grow |  -.0027387   .0032008    -0.86   0.392    -.0090121    .0035346
      intwar |   -.000734   .0103953    -0.07   0.944    -.0211083    .0196404
      civwar |  -.0069725   .0166027    -0.42   0.675    -.0395132    .0255682
    Military |   .0707154   .0133142     5.31   0.000       .04462    .0968109
   xirtpers8 |  -.0339481   .0106321    -3.19   0.001    -.0547865   -.0131097
       _cons |   .0243865   .0313267     0.78   0.436    -.0370127    .0857858
-------------+----------------------------------------------------------------
     sigma_u |  .01673623
     sigma_e |  .15161013
         rho |  .01203922   (fraction of variance due to u_i)
------------------------------------------------------------------------------

.                  est store g3

.                  xi:xtreg ged_dict $time $covar Military xirtpers8 if Monarchy==0,vc
> e(cluster cow)
i.decade          _Idecade_0-5        (naturally coded; _Idecade_0 omitted)

Random-effects GLS regression                   Number of obs     =      3,726
Group variable: cowcode                         Number of groups  =        105

R-sq:                                           Obs per group:
     within  = 0.0041                                         min =          7
     between = 0.2034                                         avg =       35.5
     overall = 0.0117                                         max =         61

                                                Wald chi2(15)     =      44.07
corr(u_i, X)   = 0 (assumed)                    Prob > chi2       =     0.0001

                              (Std. Err. adjusted for 105 clusters in cowcode)
------------------------------------------------------------------------------
             |               Robust
    ged_dict |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    ged_time |  -.0014412   .0009747    -1.48   0.139    -.0033516    .0004692
   ged_time2 |   .0000384   .0000328     1.17   0.242     -.000026    .0001028
   ged_time3 |  -2.96e-07   3.04e-07    -0.97   0.330    -8.91e-07    2.99e-07
  _Idecade_1 |    .021014   .0118215     1.78   0.075    -.0021558    .0441838
  _Idecade_2 |   .0243683   .0103315     2.36   0.018     .0041188    .0446177
  _Idecade_3 |   .0121754   .0078869     1.54   0.123    -.0032826    .0276335
  _Idecade_4 |   .0067121   .0080625     0.83   0.405    -.0090902    .0225143
  _Idecade_5 |   .0081384   .0077359     1.05   0.293    -.0070237    .0233004
     prevdem |   .0023397   .0080249     0.29   0.771    -.0133889    .0180683
        lgdp |  -.0091014    .003543    -2.57   0.010    -.0160456   -.0021572
        grow |  -.0008788    .004334    -0.20   0.839    -.0093733    .0076157
      intwar |     .01677   .0242825     0.69   0.490    -.0308229    .0643629
      civwar |    .042515   .0195803     2.17   0.030     .0041383    .0808917
    Military |   .0141396   .0098854     1.43   0.153    -.0052355    .0335146
   xirtpers8 |  -.0008524   .0111121    -0.08   0.939    -.0226318    .0209269
       _cons |   .0913377   .0276542     3.30   0.001     .0371365    .1455389
-------------+----------------------------------------------------------------
     sigma_u |          0
     sigma_e |  .16111265
         rho |          0   (fraction of variance due to u_i)
------------------------------------------------------------------------------

.                  est store g4

.                  coefplot (g1, msymbol(d)) (g2, msymbol(t)) (g3, msymbol(s)) (g4, ms
> ymbol(s)), title("Different transitions", size(medium)) ///
>                                 scheme(plottig) drop(_cons _Idecade_* $time $covar) 
> xline(0) ///
>                                 grid(glcolor(gs15)) mfcolor(white) xlabel(-.1 (.05) 
> .1)  levels(95 90) xtitle("Coefficient estimate", height(6)) ///
>                                 legend(lab(3 "Democracy, dummy") lab(6 "Dictatorship
> , dummy") lab(9 "Democracy, time-vary") lab(12 "Dictatorship, time-vary")size(vsmall
> ) pos(6) ring(1.5) col(2)) ///
>                                 ysize(1) xsize(1)       

.                 graph export "$dir/golden/Collapse-Transtions.pdf", as(pdf) replace
(file /Users/lee/Dropbox/Datavers/golden/Collapse-Transtions.pdf written in PDF format
> )

.  */
.  **************** The END ************
.  
.  log close
      name:  <unnamed>
       log:  /Users/lee/Dropbox/Datavers/Structure.log
  log type:  text
 closed on:  24 Jun 2019, 15:22:12
--------------------------------------------------------------------------------------
